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Sample records for computation inquantitative genetics

  1. Strategies for MCMC computation inquantitative genetics

    DEFF Research Database (Denmark)

    Waagepetersen, Rasmus; Ibánēz-Escriche, Noelia; Sorensen, Daniel

    another extension of the linear mixed model introducing genetic random effects influencing the log residual variances of the observations thereby producing a genetically structured variance heterogeneity. Considerable computational problems arise when abandoning the standard linear mixed model. Maximum...... the various algorithms in the context of the heterogeneous variance model. Apart from being a model of great interest in its own right, this model has proven to be a hard test for MCMC methods. We compare the performances of the different algorithms when applied to three real datasets which differ markedly...... results of applying two MCMC schemes to data sets with pig litter sizes, rabbit litter sizes, and snail weights. Some concluding remarks are given in Section 5....

  2. Quantum Genetic Algorithms for Computer Scientists

    Directory of Open Access Journals (Sweden)

    Rafael Lahoz-Beltra

    2016-10-01

    Full Text Available Genetic algorithms (GAs are a class of evolutionary algorithms inspired by Darwinian natural selection. They are popular heuristic optimisation methods based on simulated genetic mechanisms, i.e., mutation, crossover, etc. and population dynamical processes such as reproduction, selection, etc. Over the last decade, the possibility to emulate a quantum computer (a computer using quantum-mechanical phenomena to perform operations on data has led to a new class of GAs known as “Quantum Genetic Algorithms” (QGAs. In this review, we present a discussion, future potential, pros and cons of this new class of GAs. The review will be oriented towards computer scientists interested in QGAs “avoiding” the possible difficulties of quantum-mechanical phenomena.

  3. Quantum Genetic Algorithms for Computer Scientists

    OpenAIRE

    Lahoz Beltrá, Rafael

    2016-01-01

    Genetic algorithms (GAs) are a class of evolutionary algorithms inspired by Darwinian natural selection. They are popular heuristic optimisation methods based on simulated genetic mechanisms, i.e., mutation, crossover, etc. and population dynamical processes such as reproduction, selection, etc. Over the last decade, the possibility to emulate a quantum computer (a computer using quantum-mechanical phenomena to perform operations on data) has led to a new class of GAs known as “Quantum Geneti...

  4. Quantum Genetics in terms of Quantum Reversible Automata and Quantum Computation of Genetic Codes and Reverse Transcription

    CERN Document Server

    Baianu,I C

    2004-01-01

    The concepts of quantum automata and quantum computation are studied in the context of quantum genetics and genetic networks with nonlinear dynamics. In previous publications (Baianu,1971a, b) the formal concept of quantum automaton and quantum computation, respectively, were introduced and their possible implications for genetic processes and metabolic activities in living cells and organisms were considered. This was followed by a report on quantum and abstract, symbolic computation based on the theory of categories, functors and natural transformations (Baianu,1971b; 1977; 1987; 2004; Baianu et al, 2004). The notions of topological semigroup, quantum automaton, or quantum computer, were then suggested with a view to their potential applications to the analogous simulation of biological systems, and especially genetic activities and nonlinear dynamics in genetic networks. Further, detailed studies of nonlinear dynamics in genetic networks were carried out in categories of n-valued, Lukasiewicz Logic Algebra...

  5. 10th International Conference on Genetic and Evolutionary Computing

    CERN Document Server

    Lin, Jerry; Wang, Chia-Hung; Jiang, Xin

    2017-01-01

    This book gathers papers presented at the 10th International Conference on Genetic and Evolutionary Computing (ICGEC 2016). The conference was co-sponsored by Springer, Fujian University of Technology in China, the University of Computer Studies in Yangon, University of Miyazaki in Japan, National Kaohsiung University of Applied Sciences in Taiwan, Taiwan Association for Web Intelligence Consortium, and VSB-Technical University of Ostrava, Czech Republic. The ICGEC 2016, which was held from November 7 to 9, 2016 in Fuzhou City, China, was intended as an international forum for researchers and professionals in all areas of genetic and evolutionary computing.

  6. Easy calculations of lod scores and genetic risks on small computers.

    Science.gov (United States)

    Lathrop, G M; Lalouel, J M

    1984-01-01

    A computer program that calculates lod scores and genetic risks for a wide variety of both qualitative and quantitative genetic traits is discussed. An illustration is given of the joint use of a genetic marker, affection status, and quantitative information in counseling situations regarding Duchenne muscular dystrophy. PMID:6585139

  7. Genetic networks and soft computing.

    Science.gov (United States)

    Mitra, Sushmita; Das, Ranajit; Hayashi, Yoichi

    2011-01-01

    The analysis of gene regulatory networks provides enormous information on various fundamental cellular processes involving growth, development, hormone secretion, and cellular communication. Their extraction from available gene expression profiles is a challenging problem. Such reverse engineering of genetic networks offers insight into cellular activity toward prediction of adverse effects of new drugs or possible identification of new drug targets. Tasks such as classification, clustering, and feature selection enable efficient mining of knowledge about gene interactions in the form of networks. It is known that biological data is prone to different kinds of noise and ambiguity. Soft computing tools, such as fuzzy sets, evolutionary strategies, and neurocomputing, have been found to be helpful in providing low-cost, acceptable solutions in the presence of various types of uncertainties. In this paper, we survey the role of these soft methodologies and their hybridizations, for the purpose of generating genetic networks.

  8. Application of computational methods in genetic study of inflammatory bowel disease.

    Science.gov (United States)

    Li, Jin; Wei, Zhi; Hakonarson, Hakon

    2016-01-21

    Genetic factors play an important role in the etiology of inflammatory bowel disease (IBD). The launch of genome-wide association study (GWAS) represents a landmark in the genetic study of human complex disease. Concurrently, computational methods have undergone rapid development during the past a few years, which led to the identification of numerous disease susceptibility loci. IBD is one of the successful examples of GWAS and related analyses. A total of 163 genetic loci and multiple signaling pathways have been identified to be associated with IBD. Pleiotropic effects were found for many of these loci; and risk prediction models were built based on a broad spectrum of genetic variants. Important gene-gene, gene-environment interactions and key contributions of gut microbiome are being discovered. Here we will review the different types of analyses that have been applied to IBD genetic study, discuss the computational methods for each type of analysis, and summarize the discoveries made in IBD research with the application of these methods.

  9. 7th International Conference on Genetic and Evolutionary Computing

    CERN Document Server

    Krömer, Pavel; Snášel, Václav

    2014-01-01

    Genetic and Evolutionary Computing This volume of Advances in Intelligent Systems and Computing contains accepted papers presented at ICGEC 2013, the 7th International Conference on Genetic and Evolutionary Computing. The conference this year was technically co-sponsored by The Waseda University in Japan, Kaohsiung University of Applied Science in Taiwan, and VSB-Technical University of Ostrava. ICGEC 2013 was held in Prague, Czech Republic. Prague is one of the most beautiful cities in the world whose magical atmosphere has been shaped over ten centuries. Places of the greatest tourist interest are on the Royal Route running from the Powder Tower through Celetna Street to Old Town Square, then across Charles Bridge through the Lesser Town up to the Hradcany Castle. One should not miss the Jewish Town, and the National Gallery with its fine collection of Czech Gothic art, collection of old European art, and a beautiful collection of French art. The conference was intended as an international forum for the res...

  10. 8th International Conference on Genetic and Evolutionary Computing

    CERN Document Server

    Yang, Chin-Yu; Lin, Chun-Wei; Pan, Jeng-Shyang; Snasel, Vaclav; Abraham, Ajith

    2015-01-01

    This volume of Advances in Intelligent Systems and Computing contains accepted papers presented at ICGEC 2014, the 8th International Conference on Genetic and Evolutionary Computing. The conference this year was technically co-sponsored by Nanchang Institute of Technology in China, Kaohsiung University of Applied Science in Taiwan, and VSB-Technical University of Ostrava. ICGEC 2014 is held from 18-20 October 2014 in Nanchang, China. Nanchang is one of is the capital of Jiangxi Province in southeastern China, located in the north-central portion of the province. As it is bounded on the west by the Jiuling Mountains, and on the east by Poyang Lake, it is famous for its scenery, rich history and cultural sites. Because of its central location relative to the Yangtze and Pearl River Delta regions, it is a major railroad hub in Southern China. The conference is intended as an international forum for the researchers and professionals in all areas of genetic and evolutionary computing.

  11. Intelligent cloud computing security using genetic algorithm as a computational tools

    Science.gov (United States)

    Razuky AL-Shaikhly, Mazin H.

    2018-05-01

    An essential change had occurred in the field of Information Technology which represented with cloud computing, cloud giving virtual assets by means of web yet awesome difficulties in the field of information security and security assurance. Currently main problem with cloud computing is how to improve privacy and security for cloud “cloud is critical security”. This paper attempts to solve cloud security by using intelligent system with genetic algorithm as wall to provide cloud data secure, all services provided by cloud must detect who receive and register it to create list of users (trusted or un-trusted) depend on behavior. The execution of present proposal has shown great outcome.

  12. Computation of the Genetic Code

    Science.gov (United States)

    Kozlov, Nicolay N.; Kozlova, Olga N.

    2018-03-01

    One of the problems in the development of mathematical theory of the genetic code (summary is presented in [1], the detailed -to [2]) is the problem of the calculation of the genetic code. Similar problems in the world is unknown and could be delivered only in the 21st century. One approach to solving this problem is devoted to this work. For the first time provides a detailed description of the method of calculation of the genetic code, the idea of which was first published earlier [3]), and the choice of one of the most important sets for the calculation was based on an article [4]. Such a set of amino acid corresponds to a complete set of representations of the plurality of overlapping triple gene belonging to the same DNA strand. A separate issue was the initial point, triggering an iterative search process all codes submitted by the initial data. Mathematical analysis has shown that the said set contains some ambiguities, which have been founded because of our proposed compressed representation of the set. As a result, the developed method of calculation was limited to the two main stages of research, where the first stage only the of the area were used in the calculations. The proposed approach will significantly reduce the amount of computations at each step in this complex discrete structure.

  13. Use of a genetic algorithm to solve two-fluid flow problems on an NCUBE multiprocessor computer

    International Nuclear Information System (INIS)

    Pryor, R.J.; Cline, D.D.

    1992-01-01

    A method of solving the two-phase fluid flow equations using a genetic algorithm on a NCUBE multiprocessor computer is presented. The topics discussed are the two-phase flow equations, the genetic representation of the unknowns, the fitness function, the genetic operators, and the implementation of the algorithm on the NCUBE computer. The efficiency of the implementation is investigated using a pipe blowdown problem. Effects of varying the genetic parameters and the number of processors are presented

  14. Use of a genetic agorithm to solve two-fluid flow problems on an NCUBE multiprocessor computer

    International Nuclear Information System (INIS)

    Pryor, R.J.; Cline, D.D.

    1993-01-01

    A method of solving the two-phases fluid flow equations using a genetic algorithm on a NCUBE multiprocessor computer is presented. The topics discussed are the two-phase flow equations, the genetic representation of the unkowns, the fitness function, the genetic operators, and the implementation of the algorithm on the NCUBE computer. The efficiency of the implementation is investigated using a pipe blowdown problem. Effects of varying the genetic parameters and the number of processors are presented. (orig.)

  15. Computer simulation f the genetic controller for the EB flue gas treatment process

    International Nuclear Information System (INIS)

    Moroz, Z.; Bouzyk, J.; Sowinski, M.; Chmielewski, A.G.

    2001-01-01

    The use of computer genetic algorithm (GA) for driving a controller device for the industrial flue gas purification systems employing the electron beam irradiation, has been studied. As the mathematical model of the installation the properly trained artificial neural net (ANN) was used. Various cost functions and optimising strategies of the genetic code were tested. These computer simulations proved, that ANN + GA controller can be sufficiently precise and fast to be applied in real installations. (author)

  16. 4P: fast computing of population genetics statistics from large DNA polymorphism panels.

    Science.gov (United States)

    Benazzo, Andrea; Panziera, Alex; Bertorelle, Giorgio

    2015-01-01

    Massive DNA sequencing has significantly increased the amount of data available for population genetics and molecular ecology studies. However, the parallel computation of simple statistics within and between populations from large panels of polymorphic sites is not yet available, making the exploratory analyses of a set or subset of data a very laborious task. Here, we present 4P (parallel processing of polymorphism panels), a stand-alone software program for the rapid computation of genetic variation statistics (including the joint frequency spectrum) from millions of DNA variants in multiple individuals and multiple populations. It handles a standard input file format commonly used to store DNA variation from empirical or simulation experiments. The computational performance of 4P was evaluated using large SNP (single nucleotide polymorphism) datasets from human genomes or obtained by simulations. 4P was faster or much faster than other comparable programs, and the impact of parallel computing using multicore computers or servers was evident. 4P is a useful tool for biologists who need a simple and rapid computer program to run exploratory population genetics analyses in large panels of genomic data. It is also particularly suitable to analyze multiple data sets produced in simulation studies. Unix, Windows, and MacOs versions are provided, as well as the source code for easier pipeline implementations.

  17. Hard Real-Time Task Scheduling in Cloud Computing Using an Adaptive Genetic Algorithm

    Directory of Open Access Journals (Sweden)

    Amjad Mahmood

    2017-04-01

    Full Text Available In the Infrastructure-as-a-Service cloud computing model, virtualized computing resources in the form of virtual machines are provided over the Internet. A user can rent an arbitrary number of computing resources to meet their requirements, making cloud computing an attractive choice for executing real-time tasks. Economical task allocation and scheduling on a set of leased virtual machines is an important problem in the cloud computing environment. This paper proposes a greedy and a genetic algorithm with an adaptive selection of suitable crossover and mutation operations (named as AGA to allocate and schedule real-time tasks with precedence constraint on heterogamous virtual machines. A comprehensive simulation study has been done to evaluate the performance of the proposed algorithms in terms of their solution quality and efficiency. The simulation results show that AGA outperforms the greedy algorithm and non-adaptive genetic algorithm in terms of solution quality.

  18. Characterization of unknown genetic modifications using high throughput sequencing and computational subtraction

    Directory of Open Access Journals (Sweden)

    Butenko Melinka A

    2009-10-01

    Full Text Available Abstract Background When generating a genetically modified organism (GMO, the primary goal is to give a target organism one or several novel traits by using biotechnology techniques. A GMO will differ from its parental strain in that its pool of transcripts will be altered. Currently, there are no methods that are reliably able to determine if an organism has been genetically altered if the nature of the modification is unknown. Results We show that the concept of computational subtraction can be used to identify transgenic cDNA sequences from genetically modified plants. Our datasets include 454-type sequences from a transgenic line of Arabidopsis thaliana and published EST datasets from commercially relevant species (rice and papaya. Conclusion We believe that computational subtraction represents a powerful new strategy for determining if an organism has been genetically modified as well as to define the nature of the modification. Fewer assumptions have to be made compared to methods currently in use and this is an advantage particularly when working with unknown GMOs.

  19. Cloud Computing Task Scheduling Based on Cultural Genetic Algorithm

    Directory of Open Access Journals (Sweden)

    Li Jian-Wen

    2016-01-01

    Full Text Available The task scheduling strategy based on cultural genetic algorithm(CGA is proposed in order to improve the efficiency of task scheduling in the cloud computing platform, which targets at minimizing the total time and cost of task scheduling. The improved genetic algorithm is used to construct the main population space and knowledge space under cultural framework which get independent parallel evolution, forming a mechanism of mutual promotion to dispatch the cloud task. Simultaneously, in order to prevent the defects of the genetic algorithm which is easy to fall into local optimum, the non-uniform mutation operator is introduced to improve the search performance of the algorithm. The experimental results show that CGA reduces the total time and lowers the cost of the scheduling, which is an effective algorithm for the cloud task scheduling.

  20. Genetic similarity of polyploids - A new version of the computer program POPDIST (ver. 1.2.0) considers intraspecific genetic differentiation

    DEFF Research Database (Denmark)

    Tomiuk, Jürgen; Guldbrandtsen, Bernt; Loeschcke, Volker

    2009-01-01

    For evolutionary studies of polyploid species estimates of the genetic identity between species with different degrees of ploidy are particularly required because gene counting in samples of polyploid individuals often cannot be done, e.g., in triploids the phenotype AB can be genotypically either...... ABB or AAB. We recently suggested a genetic distance measure that is based on phenotype counting and made available the computer program POPDIST. The program provides maximum-likelihood estimates of the genetic identities and distances between polyploid populations, but this approach...

  1. Computational Genetic Regulatory Networks Evolvable, Self-organizing Systems

    CERN Document Server

    Knabe, Johannes F

    2013-01-01

    Genetic Regulatory Networks (GRNs) in biological organisms are primary engines for cells to enact their engagements with environments, via incessant, continually active coupling. In differentiated multicellular organisms, tremendous complexity has arisen in the course of evolution of life on earth. Engineering and science have so far achieved no working system that can compare with this complexity, depth and scope of organization. Abstracting the dynamics of genetic regulatory control to a computational framework in which artificial GRNs in artificial simulated cells differentiate while connected in a changing topology, it is possible to apply Darwinian evolution in silico to study the capacity of such developmental/differentiated GRNs to evolve. In this volume an evolutionary GRN paradigm is investigated for its evolvability and robustness in models of biological clocks, in simple differentiated multicellularity, and in evolving artificial developing 'organisms' which grow and express an ontogeny starting fr...

  2. 9th International Conference on Genetic and Evolutionary Computing

    CERN Document Server

    Lin, Jerry; Pan, Jeng-Shyang; Tin, Pyke; Yokota, Mitsuhiro; Genetic and Evolutionary Computing

    2016-01-01

    This volume of Advances in Intelligent Systems and Computing contains accepted papers presented at ICGEC 2015, the 9th International Conference on Genetic and Evolutionary Computing. The conference this year was technically co-sponsored by Ministry of Science and Technology, Myanmar, University of Computer Studies, Yangon, University of Miyazaki in Japan, Kaohsiung University of Applied Science in Taiwan, Fujian University of Technology in China and VSB-Technical University of Ostrava. ICGEC 2015 is held from 26-28, August, 2015 in Yangon, Myanmar. Yangon, the most multiethnic and cosmopolitan city in Myanmar, is the main gateway to the country. Despite being the commercial capital of Myanmar, Yangon is a city engulfed by its rich history and culture, an integration of ancient traditions and spiritual heritage. The stunning SHWEDAGON Pagoda is the center piece of Yangon city, which itself is famous for the best British colonial era architecture. Of particular interest in many shops of Bogyoke Aung San Market,...

  3. MEGA X: Molecular Evolutionary Genetics Analysis across Computing Platforms.

    Science.gov (United States)

    Kumar, Sudhir; Stecher, Glen; Li, Michael; Knyaz, Christina; Tamura, Koichiro

    2018-06-01

    The Molecular Evolutionary Genetics Analysis (Mega) software implements many analytical methods and tools for phylogenomics and phylomedicine. Here, we report a transformation of Mega to enable cross-platform use on Microsoft Windows and Linux operating systems. Mega X does not require virtualization or emulation software and provides a uniform user experience across platforms. Mega X has additionally been upgraded to use multiple computing cores for many molecular evolutionary analyses. Mega X is available in two interfaces (graphical and command line) and can be downloaded from www.megasoftware.net free of charge.

  4. From Genetics to Genetic Algorithms

    Indian Academy of Sciences (India)

    Genetic algorithms (GAs) are computational optimisation schemes with an ... The algorithms solve optimisation problems ..... Genetic Algorithms in Search, Optimisation and Machine. Learning, Addison-Wesley Publishing Company, Inc. 1989.

  5. Cloud identification using genetic algorithms and massively parallel computation

    Science.gov (United States)

    Buckles, Bill P.; Petry, Frederick E.

    1996-01-01

    As a Guest Computational Investigator under the NASA administered component of the High Performance Computing and Communication Program, we implemented a massively parallel genetic algorithm on the MasPar SIMD computer. Experiments were conducted using Earth Science data in the domains of meteorology and oceanography. Results obtained in these domains are competitive with, and in most cases better than, similar problems solved using other methods. In the meteorological domain, we chose to identify clouds using AVHRR spectral data. Four cloud speciations were used although most researchers settle for three. Results were remarkedly consistent across all tests (91% accuracy). Refinements of this method may lead to more timely and complete information for Global Circulation Models (GCMS) that are prevalent in weather forecasting and global environment studies. In the oceanographic domain, we chose to identify ocean currents from a spectrometer having similar characteristics to AVHRR. Here the results were mixed (60% to 80% accuracy). Given that one is willing to run the experiment several times (say 10), then it is acceptable to claim the higher accuracy rating. This problem has never been successfully automated. Therefore, these results are encouraging even though less impressive than the cloud experiment. Successful conclusion of an automated ocean current detection system would impact coastal fishing, naval tactics, and the study of micro-climates. Finally we contributed to the basic knowledge of GA (genetic algorithm) behavior in parallel environments. We developed better knowledge of the use of subpopulations in the context of shared breeding pools and the migration of individuals. Rigorous experiments were conducted based on quantifiable performance criteria. While much of the work confirmed current wisdom, for the first time we were able to submit conclusive evidence. The software developed under this grant was placed in the public domain. An extensive user

  6. Image-based computational quantification and visualization of genetic alterations and tumour heterogeneity.

    Science.gov (United States)

    Zhong, Qing; Rüschoff, Jan H; Guo, Tiannan; Gabrani, Maria; Schüffler, Peter J; Rechsteiner, Markus; Liu, Yansheng; Fuchs, Thomas J; Rupp, Niels J; Fankhauser, Christian; Buhmann, Joachim M; Perner, Sven; Poyet, Cédric; Blattner, Miriam; Soldini, Davide; Moch, Holger; Rubin, Mark A; Noske, Aurelia; Rüschoff, Josef; Haffner, Michael C; Jochum, Wolfram; Wild, Peter J

    2016-04-07

    Recent large-scale genome analyses of human tissue samples have uncovered a high degree of genetic alterations and tumour heterogeneity in most tumour entities, independent of morphological phenotypes and histopathological characteristics. Assessment of genetic copy-number variation (CNV) and tumour heterogeneity by fluorescence in situ hybridization (ISH) provides additional tissue morphology at single-cell resolution, but it is labour intensive with limited throughput and high inter-observer variability. We present an integrative method combining bright-field dual-colour chromogenic and silver ISH assays with an image-based computational workflow (ISHProfiler), for accurate detection of molecular signals, high-throughput evaluation of CNV, expressive visualization of multi-level heterogeneity (cellular, inter- and intra-tumour heterogeneity), and objective quantification of heterogeneous genetic deletions (PTEN) and amplifications (19q12, HER2) in diverse human tumours (prostate, endometrial, ovarian and gastric), using various tissue sizes and different scanners, with unprecedented throughput and reproducibility.

  7. Genetic origin and dispersal of the invasive soybean aphid inferred from population genetic analysis and approximate Bayesian computation.

    Science.gov (United States)

    Fang, Fang; Chen, Jing; Jiang, Li-Yun; Qu, Yan-Hua; Qiao, Ge-Xia

    2018-01-09

    Biological invasion is considered one of the most important global environmental problems. Knowledge of the source and dispersal routes of invasion could facilitate the eradication and control of invasive species. Soybean aphid, Aphis glycines Matsumura, is one of the most destructive soybean pests. For effective management of this pest, we conducted genetic analyses and approximate Bayesian computation (ABC) analysis to determine the origins and dispersal of the aphid species, as well as the source of its invasion in the USA, using eight microsatellite loci and the mitochondrial cytochrome c oxidase subunit I (COI) gene. We were able to identify a significant isolation by distance (IBD) pattern and three genetic lineages in the microsatellite data but not in the mtDNA dataset. The genetic structure showed that the USA population has the closest relationship with those from Korea and Japan, indicating that the two latter populations might be the sources of the invasion to the USA. Both population genetic analyses and ABC showed that the northeastern populations in China were the possible sources of the further spread of A. glycines to Indonesia. The dispersal history of this aphid can provide useful information for pest management strategies and can further help predict areas at risk of invasion. This article is protected by copyright. All rights reserved.

  8. Strategies for MCMC computation in quantitative genetics

    DEFF Research Database (Denmark)

    Waagepetersen, Rasmus; Ibanez, Noelia; Sorensen, Daniel

    2006-01-01

    Given observations of a trait and a pedigree for a group of animals, the basic model in quantitative genetics is a linear mixed model with genetic random effects. The correlation matrix of the genetic random effects is determined by the pedigree and is typically very highdimensional but with a sp......Given observations of a trait and a pedigree for a group of animals, the basic model in quantitative genetics is a linear mixed model with genetic random effects. The correlation matrix of the genetic random effects is determined by the pedigree and is typically very highdimensional...

  9. Computational Re-design of Synthetic Genetic Oscillators for Independent Amplitude and Frequency Modulation.

    Science.gov (United States)

    Tomazou, Marios; Barahona, Mauricio; Polizzi, Karen M; Stan, Guy-Bart

    2018-04-25

    To perform well in biotechnology applications, synthetic genetic oscillators must be engineered to allow independent modulation of amplitude and period. This need is currently unmet. Here, we demonstrate computationally how two classic genetic oscillators, the dual-feedback oscillator and the repressilator, can be re-designed to provide independent control of amplitude and period and improve tunability-that is, a broad dynamic range of periods and amplitudes accessible through the input "dials." Our approach decouples frequency and amplitude modulation by incorporating an orthogonal "sink module" where the key molecular species are channeled for enzymatic degradation. This sink module maintains fast oscillation cycles while alleviating the translational coupling between the oscillator's transcription factors and output. We characterize the behavior of our re-designed oscillators over a broad range of physiologically reasonable parameters, explain why this facilitates broader function and control, and provide general design principles for building synthetic genetic oscillators that are more precisely controllable. Copyright © 2018 The Authors. Published by Elsevier Inc. All rights reserved.

  10. PopSc: Computing Toolkit for Basic Statistics of Molecular Population Genetics Simultaneously Implemented in Web-Based Calculator, Python and R.

    Science.gov (United States)

    Chen, Shi-Yi; Deng, Feilong; Huang, Ying; Li, Cao; Liu, Linhai; Jia, Xianbo; Lai, Song-Jia

    2016-01-01

    Although various computer tools have been elaborately developed to calculate a series of statistics in molecular population genetics for both small- and large-scale DNA data, there is no efficient and easy-to-use toolkit available yet for exclusively focusing on the steps of mathematical calculation. Here, we present PopSc, a bioinformatic toolkit for calculating 45 basic statistics in molecular population genetics, which could be categorized into three classes, including (i) genetic diversity of DNA sequences, (ii) statistical tests for neutral evolution, and (iii) measures of genetic differentiation among populations. In contrast to the existing computer tools, PopSc was designed to directly accept the intermediate metadata, such as allele frequencies, rather than the raw DNA sequences or genotyping results. PopSc is first implemented as the web-based calculator with user-friendly interface, which greatly facilitates the teaching of population genetics in class and also promotes the convenient and straightforward calculation of statistics in research. Additionally, we also provide the Python library and R package of PopSc, which can be flexibly integrated into other advanced bioinformatic packages of population genetics analysis.

  11. PopSc: Computing Toolkit for Basic Statistics of Molecular Population Genetics Simultaneously Implemented in Web-Based Calculator, Python and R.

    Directory of Open Access Journals (Sweden)

    Shi-Yi Chen

    Full Text Available Although various computer tools have been elaborately developed to calculate a series of statistics in molecular population genetics for both small- and large-scale DNA data, there is no efficient and easy-to-use toolkit available yet for exclusively focusing on the steps of mathematical calculation. Here, we present PopSc, a bioinformatic toolkit for calculating 45 basic statistics in molecular population genetics, which could be categorized into three classes, including (i genetic diversity of DNA sequences, (ii statistical tests for neutral evolution, and (iii measures of genetic differentiation among populations. In contrast to the existing computer tools, PopSc was designed to directly accept the intermediate metadata, such as allele frequencies, rather than the raw DNA sequences or genotyping results. PopSc is first implemented as the web-based calculator with user-friendly interface, which greatly facilitates the teaching of population genetics in class and also promotes the convenient and straightforward calculation of statistics in research. Additionally, we also provide the Python library and R package of PopSc, which can be flexibly integrated into other advanced bioinformatic packages of population genetics analysis.

  12. Utility of computer simulations in landscape genetics

    Science.gov (United States)

    Bryan K. Epperson; Brad H. McRae; Kim Scribner; Samuel A. Cushman; Michael S. Rosenberg; Marie-Josee Fortin; Patrick M. A. James; Melanie Murphy; Stephanie Manel; Pierre Legendre; Mark R. T. Dale

    2010-01-01

    Population genetics theory is primarily based on mathematical models in which spatial complexity and temporal variability are largely ignored. In contrast, the field of landscape genetics expressly focuses on how population genetic processes are affected by complex spatial and temporal environmental heterogeneity. It is spatially explicit and relates patterns to...

  13. Strategies for MCMC computation in quantitative genetics

    DEFF Research Database (Denmark)

    Waagepetersen, Rasmus; Ibánez, N.; Sorensen, Daniel

    2006-01-01

    Given observations of a trait and a pedigree for a group of animals, the basic model in quantitative genetics is a linear mixed model with genetic random effects. The correlation matrix of the genetic random effects is determined by the pedigree and is typically very highdimensional but with a sp...

  14. Development of E-Info geneca: a website providing computer-tailored information and question prompt prior to breast cancer genetic counseling.

    NARCIS (Netherlands)

    Albada, A.; Dulmen, S. van; Otten, R.; Bensing, J.M.; Ausems, M.G.E.M.

    2009-01-01

    This article describes the stepwise development of the website ‘E-info geneca’. The website provides counselees in breast cancer genetic counseling with computer-tailored information and a question prompt prior to their first consultation. Counselees generally do not know what to expect from genetic

  15. Using genetic information while protecting the privacy of the soul.

    Science.gov (United States)

    Moor, J H

    1999-01-01

    Computing plays an important role in genetics (and vice versa). Theoretically, computing provides a conceptual model for the function and malfunction of our genetic machinery. Practically, contemporary computers and robots equipped with advanced algorithms make the revelation of the complete human genome imminent--computers are about to reveal our genetic souls for the first time. Ethically, computers help protect privacy by restricting access in sophisticated ways to genetic information. But the inexorable fact that computers will increasingly collect, analyze, and disseminate abundant amounts of genetic information made available through the genetic revolution, not to mention that inexpensive computing devices will make genetic information gathering easier, underscores the need for strong and immediate privacy legislation.

  16. COMPUTER METHODS OF GENETIC ANALYSIS.

    Directory of Open Access Journals (Sweden)

    A. L. Osipov

    2017-02-01

    Full Text Available The basic statistical methods used in conducting the genetic analysis of human traits. We studied by segregation analysis, linkage analysis and allelic associations. Developed software for the implementation of these methods support.

  17. Computer-aided identification of polymorphism sets diagnostic for groups of bacterial and viral genetic variants

    Directory of Open Access Journals (Sweden)

    Huygens Flavia

    2007-08-01

    Full Text Available Abstract Background Single nucleotide polymorphisms (SNPs and genes that exhibit presence/absence variation have provided informative marker sets for bacterial and viral genotyping. Identification of marker sets optimised for these purposes has been based on maximal generalized discriminatory power as measured by Simpson's Index of Diversity, or on the ability to identify specific variants. Here we describe the Not-N algorithm, which is designed to identify small sets of genetic markers diagnostic for user-specified subsets of known genetic variants. The algorithm does not treat the user-specified subset and the remaining genetic variants equally. Rather Not-N analysis is designed to underpin assays that provide 0% false negatives, which is very important for e.g. diagnostic procedures for clinically significant subgroups within microbial species. Results The Not-N algorithm has been incorporated into the "Minimum SNPs" computer program and used to derive genetic markers diagnostic for multilocus sequence typing-defined clonal complexes, hepatitis C virus (HCV subtypes, and phylogenetic clades defined by comparative genome hybridization (CGH data for Campylobacter jejuni, Yersinia enterocolitica and Clostridium difficile. Conclusion Not-N analysis is effective for identifying small sets of genetic markers diagnostic for microbial sub-groups. The best results to date have been obtained with CGH data from several bacterial species, and HCV sequence data.

  18. Introduction to morphogenetic computing

    CERN Document Server

    Resconi, Germano; Xu, Guanglin

    2017-01-01

    This book offers a concise introduction to morphogenetic computing, showing that its use makes global and local relations, defects in crystal non-Euclidean geometry databases with source and sink, genetic algorithms, and neural networks more stable and efficient. It also presents applications to database, language, nanotechnology with defects, biological genetic structure, electrical circuit, and big data structure. In Turing machines, input and output states form a system – when the system is in one state, the input is transformed into output. This computation is always deterministic and without any possible contradiction or defects. In natural computation there are defects and contradictions that have to be solved to give a coherent and effective computation. The new computation generates the morphology of the system that assumes different forms in time. Genetic process is the prototype of the morphogenetic computing. At the Boolean logic truth value, we substitute a set of truth (active sets) values with...

  19. [MapDraw: a microsoft excel macro for drawing genetic linkage maps based on given genetic linkage data].

    Science.gov (United States)

    Liu, Ren-Hu; Meng, Jin-Ling

    2003-05-01

    MAPMAKER is one of the most widely used computer software package for constructing genetic linkage maps.However, the PC version, MAPMAKER 3.0 for PC, could not draw the genetic linkage maps that its Macintosh version, MAPMAKER 3.0 for Macintosh,was able to do. Especially in recent years, Macintosh computer is much less popular than PC. Most of the geneticists use PC to analyze their genetic linkage data. So a new computer software to draw the same genetic linkage maps on PC as the MAPMAKER for Macintosh to do on Macintosh has been crying for. Microsoft Excel,one component of Microsoft Office package, is one of the most popular software in laboratory data processing. Microsoft Visual Basic for Applications (VBA) is one of the most powerful functions of Microsoft Excel. Using this program language, we can take creative control of Excel, including genetic linkage map construction, automatic data processing and more. In this paper, a Microsoft Excel macro called MapDraw is constructed to draw genetic linkage maps on PC computer based on given genetic linkage data. Use this software,you can freely construct beautiful genetic linkage map in Excel and freely edit and copy it to Word or other application. This software is just an Excel format file. You can freely copy it from ftp://211.69.140.177 or ftp://brassica.hzau.edu.cn and the source code can be found in Excel's Visual Basic Editor.

  20. An investigation of genetic algorithms

    International Nuclear Information System (INIS)

    Douglas, S.R.

    1995-04-01

    Genetic algorithms mimic biological evolution by natural selection in their search for better individuals within a changing population. they can be used as efficient optimizers. This report discusses the developing field of genetic algorithms. It gives a simple example of the search process and introduces the concept of schema. It also discusses modifications to the basic genetic algorithm that result in species and niche formation, in machine learning and artificial evolution of computer programs, and in the streamlining of human-computer interaction. (author). 3 refs., 1 tab., 2 figs

  1. Inference of Tumor Evolution during Chemotherapy by Computational Modeling and In Situ Analysis of Genetic and Phenotypic Cellular Diversity

    Directory of Open Access Journals (Sweden)

    Vanessa Almendro

    2014-02-01

    Full Text Available Cancer therapy exerts a strong selection pressure that shapes tumor evolution, yet our knowledge of how tumors change during treatment is limited. Here, we report the analysis of cellular heterogeneity for genetic and phenotypic features and their spatial distribution in breast tumors pre- and post-neoadjuvant chemotherapy. We found that intratumor genetic diversity was tumor-subtype specific, and it did not change during treatment in tumors with partial or no response. However, lower pretreatment genetic diversity was significantly associated with pathologic complete response. In contrast, phenotypic diversity was different between pre- and posttreatment samples. We also observed significant changes in the spatial distribution of cells with distinct genetic and phenotypic features. We used these experimental data to develop a stochastic computational model to infer tumor growth patterns and evolutionary dynamics. Our results highlight the importance of integrated analysis of genotypes and phenotypes of single cells in intact tissues to predict tumor evolution.

  2. Inference of tumor evolution during chemotherapy by computational modeling and in situ analysis of genetic and phenotypic cellular diversity

    International Nuclear Information System (INIS)

    Almendro, Vanessa; Cheng, Yu-Kang; Randles, Amanda; Itzkovitz, Shalev; Marusyk, Andriy; Ametller, Elisabet; Gonzalez-Farre, Xavier; Muñoz, Montse; Russnes, Hege G.; Helland, Åslaug; Rye, Inga H.; Borresen-Dale, Anne-Lise; Maruyama, Reo; Van Oudenaarden, Alexander; Dowsett, Mitchell; Jones, Robin L.; Reis-Filho, Jorge; Gascon, Pere; Gönen, Mithat; Michor, Franziska; Polyak, Kornelia

    2014-01-01

    Cancer therapy exerts a strong selection pressure that shapes tumor evolution, yet our knowledge of how tumors change during treatment is limited. Here, we report the analysis of cellular heterogeneity for genetic and phenotypic features and their spatial distribution in breast tumors pre- and post-neoadjuvant chemotherapy. We found that intratumor genetic diversity was tumor-subtype specific, and it did not change during treatment in tumors with partial or no response. However, lower pretreatment genetic diversity was significantly associated with pathologic complete response. In contrast, phenotypic diversity was different between pre- and post-treatment samples. We also observed significant changes in the spatial distribution of cells with distinct genetic and phenotypic features. We used these experimental data to develop a stochastic computational model to infer tumor growth patterns and evolutionary dynamics. Our results highlight the importance of integrated analysis of genotypes and phenotypes of single cells in intact tissues to predict tumor evolution

  3. Scientific discovery using genetic programming

    DEFF Research Database (Denmark)

    Keijzer, Maarten

    2001-01-01

    programming paradigm. The induction of mathematical expressions based on data is called symbolic regression. In this work, genetic programming is extended to not just fit the data i.e., get the numbers right, but also to get the dimensions right. For this units of measurement are used. The main contribution......Genetic Programming is capable of automatically inducing symbolic computer programs on the basis of a set of examples or their performance in a simulation. Mathematical expressions are a well-defined subset of symbolic computer programs and are also suitable for optimization using the genetic...... in this work can be summarized as: The symbolic expressions produced by genetic programming can be made suitable for analysis and interpretation by using units of measurements to guide or restrict the search. To achieve this, the following has been accomplished: A standard genetic programming system...

  4. Computational dissection of human episodic memory reveals mental process-specific genetic profiles.

    Science.gov (United States)

    Luksys, Gediminas; Fastenrath, Matthias; Coynel, David; Freytag, Virginie; Gschwind, Leo; Heck, Angela; Jessen, Frank; Maier, Wolfgang; Milnik, Annette; Riedel-Heller, Steffi G; Scherer, Martin; Spalek, Klara; Vogler, Christian; Wagner, Michael; Wolfsgruber, Steffen; Papassotiropoulos, Andreas; de Quervain, Dominique J-F

    2015-09-01

    Episodic memory performance is the result of distinct mental processes, such as learning, memory maintenance, and emotional modulation of memory strength. Such processes can be effectively dissociated using computational models. Here we performed gene set enrichment analyses of model parameters estimated from the episodic memory performance of 1,765 healthy young adults. We report robust and replicated associations of the amine compound SLC (solute-carrier) transporters gene set with the learning rate, of the collagen formation and transmembrane receptor protein tyrosine kinase activity gene sets with the modulation of memory strength by negative emotional arousal, and of the L1 cell adhesion molecule (L1CAM) interactions gene set with the repetition-based memory improvement. Furthermore, in a large functional MRI sample of 795 subjects we found that the association between L1CAM interactions and memory maintenance revealed large clusters of differences in brain activity in frontal cortical areas. Our findings provide converging evidence that distinct genetic profiles underlie specific mental processes of human episodic memory. They also provide empirical support to previous theoretical and neurobiological studies linking specific neuromodulators to the learning rate and linking neural cell adhesion molecules to memory maintenance. Furthermore, our study suggests additional memory-related genetic pathways, which may contribute to a better understanding of the neurobiology of human memory.

  5. Computational dissection of human episodic memory reveals mental process-specific genetic profiles

    Science.gov (United States)

    Luksys, Gediminas; Fastenrath, Matthias; Coynel, David; Freytag, Virginie; Gschwind, Leo; Heck, Angela; Jessen, Frank; Maier, Wolfgang; Milnik, Annette; Riedel-Heller, Steffi G.; Scherer, Martin; Spalek, Klara; Vogler, Christian; Wagner, Michael; Wolfsgruber, Steffen; Papassotiropoulos, Andreas; de Quervain, Dominique J.-F.

    2015-01-01

    Episodic memory performance is the result of distinct mental processes, such as learning, memory maintenance, and emotional modulation of memory strength. Such processes can be effectively dissociated using computational models. Here we performed gene set enrichment analyses of model parameters estimated from the episodic memory performance of 1,765 healthy young adults. We report robust and replicated associations of the amine compound SLC (solute-carrier) transporters gene set with the learning rate, of the collagen formation and transmembrane receptor protein tyrosine kinase activity gene sets with the modulation of memory strength by negative emotional arousal, and of the L1 cell adhesion molecule (L1CAM) interactions gene set with the repetition-based memory improvement. Furthermore, in a large functional MRI sample of 795 subjects we found that the association between L1CAM interactions and memory maintenance revealed large clusters of differences in brain activity in frontal cortical areas. Our findings provide converging evidence that distinct genetic profiles underlie specific mental processes of human episodic memory. They also provide empirical support to previous theoretical and neurobiological studies linking specific neuromodulators to the learning rate and linking neural cell adhesion molecules to memory maintenance. Furthermore, our study suggests additional memory-related genetic pathways, which may contribute to a better understanding of the neurobiology of human memory. PMID:26261317

  6. A general model for likelihood computations of genetic marker data accounting for linkage, linkage disequilibrium, and mutations.

    Science.gov (United States)

    Kling, Daniel; Tillmar, Andreas; Egeland, Thore; Mostad, Petter

    2015-09-01

    Several applications necessitate an unbiased determination of relatedness, be it in linkage or association studies or in a forensic setting. An appropriate model to compute the joint probability of some genetic data for a set of persons given some hypothesis about the pedigree structure is then required. The increasing number of markers available through high-density SNP microarray typing and NGS technologies intensifies the demand, where using a large number of markers may lead to biased results due to strong dependencies between closely located loci, both within pedigrees (linkage) and in the population (allelic association or linkage disequilibrium (LD)). We present a new general model, based on a Markov chain for inheritance patterns and another Markov chain for founder allele patterns, the latter allowing us to account for LD. We also demonstrate a specific implementation for X chromosomal markers that allows for computation of likelihoods based on hypotheses of alleged relationships and genetic marker data. The algorithm can simultaneously account for linkage, LD, and mutations. We demonstrate its feasibility using simulated examples. The algorithm is implemented in the software FamLinkX, providing a user-friendly GUI for Windows systems (FamLinkX, as well as further usage instructions, is freely available at www.famlink.se ). Our software provides the necessary means to solve cases where no previous implementation exists. In addition, the software has the possibility to perform simulations in order to further study the impact of linkage and LD on computed likelihoods for an arbitrary set of markers.

  7. A genetic-algorithm-based method to find unitary transformations for any desired quantum computation and application to a one-bit oracle decision problem

    Energy Technology Data Exchange (ETDEWEB)

    Bang, Jeongho [Seoul National University, Seoul (Korea, Republic of); Hanyang University, Seoul (Korea, Republic of); Yoo, Seokwon [Hanyang University, Seoul (Korea, Republic of)

    2014-12-15

    We propose a genetic-algorithm-based method to find the unitary transformations for any desired quantum computation. We formulate a simple genetic algorithm by introducing the 'genetic parameter vector' of the unitary transformations to be found. In the genetic algorithm process, all components of the genetic parameter vectors are supposed to evolve to the solution parameters of the unitary transformations. We apply our method to find the optimal unitary transformations and to generalize the corresponding quantum algorithms for a realistic problem, the one-bit oracle decision problem, or the often-called Deutsch problem. By numerical simulations, we can faithfully find the appropriate unitary transformations to solve the problem by using our method. We analyze the quantum algorithms identified by the found unitary transformations and generalize the variant models of the original Deutsch's algorithm.

  8. Journal of Genetics | Indian Academy of Sciences

    Indian Academy of Sciences (India)

    Home; Journals; Journal of Genetics; Volume 80; Issue 1. Testing quantum dynamics in genetic information processing ... Keywords. assembly; computation; database search; DNA replication; genetic information; nucleotide base; polymerase enzyme; quantum coherence; quantum mechanics; quantum superposition.

  9. Genetic algorithms applied to nuclear reactor design optimization

    International Nuclear Information System (INIS)

    Pereira, C.M.N.A.; Schirru, R.; Martinez, A.S.

    2000-01-01

    A genetic algorithm is a powerful search technique that simulates natural evolution in order to fit a population of computational structures to the solution of an optimization problem. This technique presents several advantages over classical ones such as linear programming based techniques, often used in nuclear engineering optimization problems. However, genetic algorithms demand some extra computational cost. Nowadays, due to the fast computers available, the use of genetic algorithms has increased and its practical application has become a reality. In nuclear engineering there are many difficult optimization problems related to nuclear reactor design. Genetic algorithm is a suitable technique to face such kind of problems. This chapter presents applications of genetic algorithms for nuclear reactor core design optimization. A genetic algorithm has been designed to optimize the nuclear reactor cell parameters, such as array pitch, isotopic enrichment, dimensions and cells materials. Some advantages of this genetic algorithm implementation over a classical method based on linear programming are revealed through the application of both techniques to a simple optimization problem. In order to emphasize the suitability of genetic algorithms for design optimization, the technique was successfully applied to a more complex problem, where the classical method is not suitable. Results and comments about the applications are also presented. (orig.)

  10. Chapter 10: Mining genome-wide genetic markers.

    Directory of Open Access Journals (Sweden)

    Xiang Zhang

    Full Text Available Genome-wide association study (GWAS aims to discover genetic factors underlying phenotypic traits. The large number of genetic factors poses both computational and statistical challenges. Various computational approaches have been developed for large scale GWAS. In this chapter, we will discuss several widely used computational approaches in GWAS. The following topics will be covered: (1 An introduction to the background of GWAS. (2 The existing computational approaches that are widely used in GWAS. This will cover single-locus, epistasis detection, and machine learning methods that have been recently developed in biology, statistic, and computer science communities. This part will be the main focus of this chapter. (3 The limitations of current approaches and future directions.

  11. A Robust Computational Technique for Model Order Reduction of Two-Time-Scale Discrete Systems via Genetic Algorithms

    Directory of Open Access Journals (Sweden)

    Othman M. K. Alsmadi

    2015-01-01

    Full Text Available A robust computational technique for model order reduction (MOR of multi-time-scale discrete systems (single input single output (SISO and multi-input multioutput (MIMO is presented in this paper. This work is motivated by the singular perturbation of multi-time-scale systems where some specific dynamics may not have significant influence on the overall system behavior. The new approach is proposed using genetic algorithms (GA with the advantage of obtaining a reduced order model, maintaining the exact dominant dynamics in the reduced order, and minimizing the steady state error. The reduction process is performed by obtaining an upper triangular transformed matrix of the system state matrix defined in state space representation along with the elements of B, C, and D matrices. The GA computational procedure is based on maximizing the fitness function corresponding to the response deviation between the full and reduced order models. The proposed computational intelligence MOR method is compared to recently published work on MOR techniques where simulation results show the potential and advantages of the new approach.

  12. Graphical models for genetic analyses

    DEFF Research Database (Denmark)

    Lauritzen, Steffen Lilholt; Sheehan, Nuala A.

    2003-01-01

    This paper introduces graphical models as a natural environment in which to formulate and solve problems in genetics and related areas. Particular emphasis is given to the relationships among various local computation algorithms which have been developed within the hitherto mostly separate areas...... of graphical models and genetics. The potential of graphical models is explored and illustrated through a number of example applications where the genetic element is substantial or dominating....

  13. MEGA-CC: computing core of molecular evolutionary genetics analysis program for automated and iterative data analysis.

    Science.gov (United States)

    Kumar, Sudhir; Stecher, Glen; Peterson, Daniel; Tamura, Koichiro

    2012-10-15

    There is a growing need in the research community to apply the molecular evolutionary genetics analysis (MEGA) software tool for batch processing a large number of datasets and to integrate it into analysis workflows. Therefore, we now make available the computing core of the MEGA software as a stand-alone executable (MEGA-CC), along with an analysis prototyper (MEGA-Proto). MEGA-CC provides users with access to all the computational analyses available through MEGA's graphical user interface version. This includes methods for multiple sequence alignment, substitution model selection, evolutionary distance estimation, phylogeny inference, substitution rate and pattern estimation, tests of natural selection and ancestral sequence inference. Additionally, we have upgraded the source code for phylogenetic analysis using the maximum likelihood methods for parallel execution on multiple processors and cores. Here, we describe MEGA-CC and outline the steps for using MEGA-CC in tandem with MEGA-Proto for iterative and automated data analysis. http://www.megasoftware.net/.

  14. Identifying shared genetic structure patterns among Pacific Northwest forest taxa: insights from use of visualization tools and computer simulations.

    Directory of Open Access Journals (Sweden)

    Mark P Miller

    2010-10-01

    Full Text Available Identifying causal relationships in phylogeographic and landscape genetic investigations is notoriously difficult, but can be facilitated by use of multispecies comparisons.We used data visualizations to identify common spatial patterns within single lineages of four taxa inhabiting Pacific Northwest forests (northern spotted owl: Strix occidentalis caurina; red tree vole: Arborimus longicaudus; southern torrent salamander: Rhyacotriton variegatus; and western white pine: Pinus monticola. Visualizations suggested that, despite occupying the same geographical region and habitats, species responded differently to prevailing historical processes. S. o. caurina and P. monticola demonstrated directional patterns of spatial genetic structure where genetic distances and diversity were greater in southern versus northern locales. A. longicaudus and R. variegatus displayed opposite patterns where genetic distances were greater in northern versus southern regions. Statistical analyses of directional patterns subsequently confirmed observations from visualizations. Based upon regional climatological history, we hypothesized that observed latitudinal patterns may have been produced by range expansions. Subsequent computer simulations confirmed that directional patterns can be produced by expansion events.We discuss phylogeographic hypotheses regarding historical processes that may have produced observed patterns. Inferential methods used here may become increasingly powerful as detailed simulations of organisms and historical scenarios become plausible. We further suggest that inter-specific comparisons of historical patterns take place prior to drawing conclusions regarding effects of current anthropogenic change within landscapes.

  15. Computational Integration of Human Genetic Data to Evaluate AOP-Specific Susceptibility

    Science.gov (United States)

    There is a need for approaches to efficiently evaluate human genetic variability and susceptibility related to environmental chemical exposure. Direct estimation of the genetic contribution to variability in susceptibility to environmental chemicals is only possible in special ca...

  16. gPGA: GPU Accelerated Population Genetics Analyses.

    Directory of Open Access Journals (Sweden)

    Chunbao Zhou

    Full Text Available The isolation with migration (IM model is important for studies in population genetics and phylogeography. IM program applies the IM model to genetic data drawn from a pair of closely related populations or species based on Markov chain Monte Carlo (MCMC simulations of gene genealogies. But computational burden of IM program has placed limits on its application.With strong computational power, Graphics Processing Unit (GPU has been widely used in many fields. In this article, we present an effective implementation of IM program on one GPU based on Compute Unified Device Architecture (CUDA, which we call gPGA.Compared with IM program, gPGA can achieve up to 52.30X speedup on one GPU. The evaluation results demonstrate that it allows datasets to be analyzed effectively and rapidly for research on divergence population genetics. The software is freely available with source code at https://github.com/chunbaozhou/gPGA.

  17. Developing robotic behavior using a genetic programming model

    International Nuclear Information System (INIS)

    Pryor, R.J.

    1998-01-01

    This report describes the methodology for using a genetic programming model to develop tracking behaviors for autonomous, microscale robotic vehicles. The use of such vehicles for surveillance and detection operations has become increasingly important in defense and humanitarian applications. Through an evolutionary process similar to that found in nature, the genetic programming model generates a computer program that when downloaded onto a robotic vehicle's on-board computer will guide the robot to successfully accomplish its task. Simulations of multiple robots engaged in problem-solving tasks have demonstrated cooperative behaviors. This report also discusses the behavior model produced by genetic programming and presents some results achieved during the study

  18. Synthetic Computation: Chaos Computing, Logical Stochastic Resonance, and Adaptive Computing

    Science.gov (United States)

    Kia, Behnam; Murali, K.; Jahed Motlagh, Mohammad-Reza; Sinha, Sudeshna; Ditto, William L.

    Nonlinearity and chaos can illustrate numerous behaviors and patterns, and one can select different patterns from this rich library of patterns. In this paper we focus on synthetic computing, a field that engineers and synthesizes nonlinear systems to obtain computation. We explain the importance of nonlinearity, and describe how nonlinear systems can be engineered to perform computation. More specifically, we provide an overview of chaos computing, a field that manually programs chaotic systems to build different types of digital functions. Also we briefly describe logical stochastic resonance (LSR), and then extend the approach of LSR to realize combinational digital logic systems via suitable concatenation of existing logical stochastic resonance blocks. Finally we demonstrate how a chaotic system can be engineered and mated with different machine learning techniques, such as artificial neural networks, random searching, and genetic algorithm, to design different autonomous systems that can adapt and respond to environmental conditions.

  19. Theory and Practice in Quantitative Genetics

    DEFF Research Database (Denmark)

    Posthuma, Daniëlle; Beem, A Leo; de Geus, Eco J C

    2003-01-01

    With the rapid advances in molecular biology, the near completion of the human genome, the development of appropriate statistical genetic methods and the availability of the necessary computing power, the identification of quantitative trait loci has now become a realistic prospect for quantitative...... geneticists. We briefly describe the theoretical biometrical foundations underlying quantitative genetics. These theoretical underpinnings are translated into mathematical equations that allow the assessment of the contribution of observed (using DNA samples) and unobserved (using known genetic relationships......) genetic variation to population variance in quantitative traits. Several statistical models for quantitative genetic analyses are described, such as models for the classical twin design, multivariate and longitudinal genetic analyses, extended twin analyses, and linkage and association analyses. For each...

  20. Comparison of genetic algorithms with conjugate gradient methods

    Science.gov (United States)

    Bosworth, J. L.; Foo, N. Y.; Zeigler, B. P.

    1972-01-01

    Genetic algorithms for mathematical function optimization are modeled on search strategies employed in natural adaptation. Comparisons of genetic algorithms with conjugate gradient methods, which were made on an IBM 1800 digital computer, show that genetic algorithms display superior performance over gradient methods for functions which are poorly behaved mathematically, for multimodal functions, and for functions obscured by additive random noise. Genetic methods offer performance comparable to gradient methods for many of the standard functions.

  1. Computational neurogenetic modeling

    CERN Document Server

    Benuskova, Lubica

    2010-01-01

    Computational Neurogenetic Modeling is a student text, introducing the scope and problems of a new scientific discipline - Computational Neurogenetic Modeling (CNGM). CNGM is concerned with the study and development of dynamic neuronal models for modeling brain functions with respect to genes and dynamic interactions between genes. These include neural network models and their integration with gene network models. This new area brings together knowledge from various scientific disciplines, such as computer and information science, neuroscience and cognitive science, genetics and molecular biol

  2. A reconfigurable NAND/NOR genetic logic gate.

    Science.gov (United States)

    Goñi-Moreno, Angel; Amos, Martyn

    2012-09-18

    Engineering genetic Boolean logic circuits is a major research theme of synthetic biology. By altering or introducing connections between genetic components, novel regulatory networks are built in order to mimic the behaviour of electronic devices such as logic gates. While electronics is a highly standardized science, genetic logic is still in its infancy, with few agreed standards. In this paper we focus on the interpretation of logical values in terms of molecular concentrations. We describe the results of computational investigations of a novel circuit that is able to trigger specific differential responses depending on the input standard used. The circuit can therefore be dynamically reconfigured (without modification) to serve as both a NAND/NOR logic gate. This multi-functional behaviour is achieved by a) varying the meanings of inputs, and b) using branch predictions (as in computer science) to display a constrained output. A thorough computational study is performed, which provides valuable insights for the future laboratory validation. The simulations focus on both single-cell and population behaviours. The latter give particular insights into the spatial behaviour of our engineered cells on a surface with a non-homogeneous distribution of inputs. We present a dynamically-reconfigurable NAND/NOR genetic logic circuit that can be switched between modes of operation via a simple shift in input signal concentration. The circuit addresses important issues in genetic logic that will have significance for more complex synthetic biology applications.

  3. Routine human-competitive machine intelligence by means of genetic programming

    Science.gov (United States)

    Koza, John R.; Streeter, Matthew J.; Keane, Martin

    2004-01-01

    Genetic programming is a systematic method for getting computers to automatically solve a problem. Genetic programming starts from a high-level statement of what needs to be done and automatically creates a computer program to solve the problem. The paper demonstrates that genetic programming (1) now routinely delivers high-return human-competitive machine intelligence; (2) is an automated invention machine; (3) can automatically create a general solution to a problem in the form of a parameterized topology; and (4) has delivered a progression of qualitatively more substantial results in synchrony with five approximately order-of-magnitude increases in the expenditure of computer time. Recent results involving the automatic synthesis of the topology and sizing of analog electrical circuits and controllers demonstrate these points.

  4. Evolutionary Computation Methods and their applications in Statistics

    Directory of Open Access Journals (Sweden)

    Francesco Battaglia

    2013-05-01

    Full Text Available A brief discussion of the genesis of evolutionary computation methods, their relationship to artificial intelligence, and the contribution of genetics and Darwin’s theory of natural evolution is provided. Then, the main evolutionary computation methods are illustrated: evolution strategies, genetic algorithms, estimation of distribution algorithms, differential evolution, and a brief description of some evolutionary behavior methods such as ant colony and particle swarm optimization. We also discuss the role of the genetic algorithm for multivariate probability distribution random generation, rather than as a function optimizer. Finally, some relevant applications of genetic algorithm to statistical problems are reviewed: selection of variables in regression, time series model building, outlier identification, cluster analysis, design of experiments.

  5. Genetic Homogenization of Composite Materials

    Directory of Open Access Journals (Sweden)

    P. Tobola

    2009-04-01

    Full Text Available The paper is focused on numerical studies of electromagnetic properties of composite materials used for the construction of small airplanes. Discussions concentrate on the genetic homogenization of composite layers and composite layers with a slot. The homogenization is aimed to reduce CPU-time demands of EMC computational models of electrically large airplanes. First, a methodology of creating a 3-dimensional numerical model of a composite material in CST Microwave Studio is proposed focusing on a sufficient accuracy of the model. Second, a proper implementation of a genetic optimization in Matlab is discussed. Third, an association of the optimization script and a simplified 2-dimensional model of the homogeneous equivalent model in Comsol Multiphysics is proposed considering EMC issues. Results of computations are experimentally verified.

  6. Developmental system at the crossroads of system theory, computer science, and genetic engineering

    CERN Document Server

    Węgrzyn, Stefan; Vidal, Pierre

    1990-01-01

    Many facts were at the origin of the present monograph. The ftrst is the beauty of maple leaves in Quebec forests in Fall. It raised the question: how does nature create and reproduce such beautiful patterns? The second was the reading of A. Lindenmayer's works on L systems. Finally came the discovery of "the secrets of DNA" together with many stimulating ex­ changes with biologists. Looking at such facts from the viewpoint of recursive numerical systems led to devise a simple model based on six elementary operations organized in a generating word, the analog of the program of a computer and of the genetic code of DNA in the cells of a living organism. It turned out that such a model, despite its simplicity, can account for a great number of properties of living organisms, e.g. their hierarchical structure, their ability to regenerate after a trauma, the possibility of cloning, their sensitivity to mutation, their growth, decay and reproduction. The model lends itself to analysis: the knowledge of the genera...

  7. Computational thermodynamics and genetic algorithms to design affordable γ′-strengthened nickel–iron based superalloys

    International Nuclear Information System (INIS)

    Tancret, F

    2012-01-01

    Computational thermodynamics based on the CALPHAD approach (Thermo-Calc software) are used to design creep-resistant and affordable superalloys for large-scale applications such as power plants. Cost is reduced by the introduction of iron and by avoiding the use of expensive alloying elements such as Nb, Ta, Mo, Co etc. Strengthening is ensured by the addition of W, and of Al and Ti to provoke the precipitation of γ′. However, the addition of iron reduces the maximum possible volume fraction of γ′. The latter is maximized automatically using a genetic algorithm during simulation, while keeping the alloys free of undesirable phases at high temperatures. New superalloys with 20 wt% Cr are designed, with Fe content up to 37 wt%. They should be forgeable, weldable, oxidation resistant and significantly cheaper than existing alloys with equivalent properties. (paper)

  8. Linear genetic programming

    CERN Document Server

    Brameier, Markus

    2007-01-01

    Presents a variant of Genetic Programming that evolves imperative computer programs as linear sequences of instructions, in contrast to the more traditional functional expressions or syntax trees. This book serves as a reference for researchers, but also contains sufficient introduction for students and those who are new to the field

  9. Applicability of genetic algorithms to parameter estimation of economic models

    Directory of Open Access Journals (Sweden)

    Marcel Ševela

    2004-01-01

    Full Text Available The paper concentrates on capability of genetic algorithms for parameter estimation of non-linear economic models. In the paper we test the ability of genetic algorithms to estimate of parameters of demand function for durable goods and simultaneously search for parameters of genetic algorithm that lead to maximum effectiveness of the computation algorithm. The genetic algorithms connect deterministic iterative computation methods with stochastic methods. In the genteic aůgorithm approach each possible solution is represented by one individual, those life and lifes of all generations of individuals run under a few parameter of genetic algorithm. Our simulations resulted in optimal mutation rate of 15% of all bits in chromosomes, optimal elitism rate 20%. We can not set the optimal extend of generation, because it proves positive correlation with effectiveness of genetic algorithm in all range under research, but its impact is degreasing. The used genetic algorithm was sensitive to mutation rate at most, than to extend of generation. The sensitivity to elitism rate is not so strong.

  10. Applying genetic algorithms for programming manufactoring cell tasks

    Directory of Open Access Journals (Sweden)

    Efredy Delgado

    2005-05-01

    Full Text Available This work was aimed for developing computational intelligence for scheduling a manufacturing cell's tasks, based manily on genetic algorithms. The manufacturing cell was modelled as beign a production-line; the makespan was calculated by using heuristics adapted from several libraries for genetic algorithms computed in C++ builder. Several problems dealing with small, medium and large list of jobs and machinery were resolved. The results were compared with other heuristics. The approach developed here would seem to be promising for future research concerning scheduling manufacturing cell tasks involving mixed batches.

  11. Hardware for soft computing and soft computing for hardware

    CERN Document Server

    Nedjah, Nadia

    2014-01-01

    Single and Multi-Objective Evolutionary Computation (MOEA),  Genetic Algorithms (GAs), Artificial Neural Networks (ANNs), Fuzzy Controllers (FCs), Particle Swarm Optimization (PSO) and Ant colony Optimization (ACO) are becoming omnipresent in almost every intelligent system design. Unfortunately, the application of the majority of these techniques is complex and so requires a huge computational effort to yield useful and practical results. Therefore, dedicated hardware for evolutionary, neural and fuzzy computation is a key issue for designers. With the spread of reconfigurable hardware such as FPGAs, digital as well as analog hardware implementations of such computation become cost-effective. The idea behind this book is to offer a variety of hardware designs for soft computing techniques that can be embedded in any final product. Also, to introduce the successful application of soft computing technique to solve many hard problem encountered during the design of embedded hardware designs. Reconfigurable em...

  12. A Flexible Computational Framework Using R and Map-Reduce for Permutation Tests of Massive Genetic Analysis of Complex Traits.

    Science.gov (United States)

    Mahjani, Behrang; Toor, Salman; Nettelblad, Carl; Holmgren, Sverker

    2017-01-01

    In quantitative trait locus (QTL) mapping significance of putative QTL is often determined using permutation testing. The computational needs to calculate the significance level are immense, 10 4 up to 10 8 or even more permutations can be needed. We have previously introduced the PruneDIRECT algorithm for multiple QTL scan with epistatic interactions. This algorithm has specific strengths for permutation testing. Here, we present a flexible, parallel computing framework for identifying multiple interacting QTL using the PruneDIRECT algorithm which uses the map-reduce model as implemented in Hadoop. The framework is implemented in R, a widely used software tool among geneticists. This enables users to rearrange algorithmic steps to adapt genetic models, search algorithms, and parallelization steps to their needs in a flexible way. Our work underlines the maturity of accessing distributed parallel computing for computationally demanding bioinformatics applications through building workflows within existing scientific environments. We investigate the PruneDIRECT algorithm, comparing its performance to exhaustive search and DIRECT algorithm using our framework on a public cloud resource. We find that PruneDIRECT is vastly superior for permutation testing, and perform 2 ×10 5 permutations for a 2D QTL problem in 15 hours, using 100 cloud processes. We show that our framework scales out almost linearly for a 3D QTL search.

  13. Extreme Scale Computing Studies

    Science.gov (United States)

    2010-12-01

    systems that would fall under the Exascale rubric . In this chapter, we first discuss the attributes by which achievement of the label “Exascale” may be...Carrington, and E. Strohmaier. A Genetic Algorithms Approach to Modeling the Performance of Memory-bound Computations. Reno, NV, November 2007. ACM/IEEE... genetic stochasticity (random mating, mutation, etc). Outcomes are thus stochastic as well, and ecologists wish to ask questions like, “What is the

  14. The genetic and economic effect of preliminary culling in the seedling orchard

    Science.gov (United States)

    Don E. Riemenschneider

    1977-01-01

    The genetic and economic effects of two stages of truncation selection in a white spruce seedling orchard were investigated by computer simulation. Genetic effects were computed by assuming a bivariate distribution of juvenile and mature traits and volume was used as the selection criterion. Seed production was assumed to rise in a linear fashion to maturity and then...

  15. Classical and quantum computing with C++ and Java simulations

    CERN Document Server

    Hardy, Y

    2001-01-01

    Classical and Quantum computing provides a self-contained, systematic and comprehensive introduction to all the subjects and techniques important in scientific computing. The style and presentation are readily accessible to undergraduates and graduates. A large number of examples, accompanied by complete C++ and Java code wherever possible, cover every topic. Features and benefits: - Comprehensive coverage of the theory with many examples - Topics in classical computing include boolean algebra, gates, circuits, latches, error detection and correction, neural networks, Turing machines, cryptography, genetic algorithms - For the first time, genetic expression programming is presented in a textbook - Topics in quantum computing include mathematical foundations, quantum algorithms, quantum information theory, hardware used in quantum computing This book serves as a textbook for courses in scientific computing and is also very suitable for self-study. Students, professionals and practitioners in computer...

  16. Developments in statistical analysis in quantitative genetics

    DEFF Research Database (Denmark)

    Sorensen, Daniel

    2009-01-01

    of genetic means and variances, models for the analysis of categorical and count data, the statistical genetics of a model postulating that environmental variance is partly under genetic control, and a short discussion of models that incorporate massive genetic marker information. We provide an overview......A remarkable research impetus has taken place in statistical genetics since the last World Conference. This has been stimulated by breakthroughs in molecular genetics, automated data-recording devices and computer-intensive statistical methods. The latter were revolutionized by the bootstrap...... and by Markov chain Monte Carlo (McMC). In this overview a number of specific areas are chosen to illustrate the enormous flexibility that McMC has provided for fitting models and exploring features of data that were previously inaccessible. The selected areas are inferences of the trajectories over time...

  17. Heterosis Is a Systemic Property Emerging From Non-linear Genotype-Phenotype Relationships: Evidence From in Vitro Genetics and Computer Simulations

    Directory of Open Access Journals (Sweden)

    Julie B. Fiévet

    2018-05-01

    Full Text Available Heterosis, the superiority of hybrids over their parents for quantitative traits, represents a crucial issue in plant and animal breeding as well as evolutionary biology. Heterosis has given rise to countless genetic, genomic and molecular studies, but has rarely been investigated from the point of view of systems biology. We hypothesized that heterosis is an emergent property of living systems resulting from frequent concave relationships between genotypic variables and phenotypes, or between different phenotypic levels. We chose the enzyme-flux relationship as a model of the concave genotype-phenotype (GP relationship, and showed that heterosis can be easily created in the laboratory. First, we reconstituted in vitro the upper part of glycolysis. We simulated genetic variability of enzyme activity by varying enzyme concentrations in test tubes. Mixing the content of “parental” tubes resulted in “hybrids,” whose fluxes were compared to the parental fluxes. Frequent heterotic fluxes were observed, under conditions that were determined analytically and confirmed by computer simulation. Second, to test this model in a more realistic situation, we modeled the glycolysis/fermentation network in yeast by considering one input flux, glucose, and two output fluxes, glycerol and acetaldehyde. We simulated genetic variability by randomly drawing parental enzyme concentrations under various conditions, and computed the parental and hybrid fluxes using a system of differential equations. Again we found that a majority of hybrids exhibited positive heterosis for metabolic fluxes. Cases of negative heterosis were due to local convexity between certain enzyme concentrations and fluxes. In both approaches, heterosis was maximized when the parents were phenotypically close and when the distributions of parental enzyme concentrations were contrasted and constrained. These conclusions are not restricted to metabolic systems: they only depend on the

  18. Pattern recognition, neural networks, genetic algorithms and high performance computing in nuclear reactor diagnostics. Results and perspectives

    International Nuclear Information System (INIS)

    Dzwinel, W.; Pepyolyshev, N.

    1996-01-01

    The main goal of this paper is the presentation of our experience in development of the diagnostic system for the IBR-2 (Russia - Dubna) nuclear reactor. The authors show the principal results of the system modifications to make it work more reliable and much faster. The former needs the adaptation of new techniques of data processing, the latter, implementation of the newest computational facilities. The results of application of the clustering techniques and a method of visualization of the multi-dimensional information directly on the operator display are presented. The experiences with neural nets, used for prediction of the reactor operation, are discussed. The genetic algorithms were also tested, to reduce the quantity of data nd extracting the most informative components of the analyzed spectra. (authors)

  19. Computational engineering

    CERN Document Server

    2014-01-01

    The book presents state-of-the-art works in computational engineering. Focus is on mathematical modeling, numerical simulation, experimental validation and visualization in engineering sciences. In particular, the following topics are presented: constitutive models and their implementation into finite element codes, numerical models in nonlinear elasto-dynamics including seismic excitations, multiphase models in structural engineering and multiscale models of materials systems, sensitivity and reliability analysis of engineering structures, the application of scientific computing in urban water management and hydraulic engineering, and the application of genetic algorithms for the registration of laser scanner point clouds.

  20. Latent spatial models and sampling design for landscape genetics

    Science.gov (United States)

    Hanks, Ephraim M.; Hooten, Mevin B.; Knick, Steven T.; Oyler-McCance, Sara J.; Fike, Jennifer A.; Cross, Todd B.; Schwartz, Michael K.

    2016-01-01

    We propose a spatially-explicit approach for modeling genetic variation across space and illustrate how this approach can be used to optimize spatial prediction and sampling design for landscape genetic data. We propose a multinomial data model for categorical microsatellite allele data commonly used in landscape genetic studies, and introduce a latent spatial random effect to allow for spatial correlation between genetic observations. We illustrate how modern dimension reduction approaches to spatial statistics can allow for efficient computation in landscape genetic statistical models covering large spatial domains. We apply our approach to propose a retrospective spatial sampling design for greater sage-grouse (Centrocercus urophasianus) population genetics in the western United States.

  1. Naturally selecting solutions: the use of genetic algorithms in bioinformatics.

    Science.gov (United States)

    Manning, Timmy; Sleator, Roy D; Walsh, Paul

    2013-01-01

    For decades, computer scientists have looked to nature for biologically inspired solutions to computational problems; ranging from robotic control to scheduling optimization. Paradoxically, as we move deeper into the post-genomics era, the reverse is occurring, as biologists and bioinformaticians look to computational techniques, to solve a variety of biological problems. One of the most common biologically inspired techniques are genetic algorithms (GAs), which take the Darwinian concept of natural selection as the driving force behind systems for solving real world problems, including those in the bioinformatics domain. Herein, we provide an overview of genetic algorithms and survey some of the most recent applications of this approach to bioinformatics based problems.

  2. Machine learning in genetics and genomics

    Science.gov (United States)

    Libbrecht, Maxwell W.; Noble, William Stafford

    2016-01-01

    The field of machine learning promises to enable computers to assist humans in making sense of large, complex data sets. In this review, we outline some of the main applications of machine learning to genetic and genomic data. In the process, we identify some recurrent challenges associated with this type of analysis and provide general guidelines to assist in the practical application of machine learning to real genetic and genomic data. PMID:25948244

  3. Applications of genetic programming in cancer research.

    Science.gov (United States)

    Worzel, William P; Yu, Jianjun; Almal, Arpit A; Chinnaiyan, Arul M

    2009-02-01

    The theory of Darwinian evolution is the fundamental keystones of modern biology. Late in the last century, computer scientists began adapting its principles, in particular natural selection, to complex computational challenges, leading to the emergence of evolutionary algorithms. The conceptual model of selective pressure and recombination in evolutionary algorithms allow scientists to efficiently search high dimensional space for solutions to complex problems. In the last decade, genetic programming has been developed and extensively applied for analysis of molecular data to classify cancer subtypes and characterize the mechanisms of cancer pathogenesis and development. This article reviews current successes using genetic programming and discusses its potential impact in cancer research and treatment in the near future.

  4. The genetics of shovel shape in maxillary central incisors in man.

    Science.gov (United States)

    Blanco, R; Chakraborty, R

    1976-03-01

    From dental casts of 94 parent-offspring and 127 full-sib pairs, sampled from two Chilean populations, shovelling indices are computed to measure the degree of shovelling of maxillary central incisors quantitatively. Genetic correlations are computed to determine the role of genetic factors in explaining the variation in this trait. Assuming only hereditary factors to be responsible for the transmission of shovel shape, 68% of total variability is ascribed to the additive effect of genes.

  5. High-Speed General Purpose Genetic Algorithm Processor.

    Science.gov (United States)

    Hoseini Alinodehi, Seyed Pourya; Moshfe, Sajjad; Saber Zaeimian, Masoumeh; Khoei, Abdollah; Hadidi, Khairollah

    2016-07-01

    In this paper, an ultrafast steady-state genetic algorithm processor (GAP) is presented. Due to the heavy computational load of genetic algorithms (GAs), they usually take a long time to find optimum solutions. Hardware implementation is a significant approach to overcome the problem by speeding up the GAs procedure. Hence, we designed a digital CMOS implementation of GA in [Formula: see text] process. The proposed processor is not bounded to a specific application. Indeed, it is a general-purpose processor, which is capable of performing optimization in any possible application. Utilizing speed-boosting techniques, such as pipeline scheme, parallel coarse-grained processing, parallel fitness computation, parallel selection of parents, dual-population scheme, and support for pipelined fitness computation, the proposed processor significantly reduces the processing time. Furthermore, by relying on a built-in discard operator the proposed hardware may be used in constrained problems that are very common in control applications. In the proposed design, a large search space is achievable through the bit string length extension of individuals in the genetic population by connecting the 32-bit GAPs. In addition, the proposed processor supports parallel processing, in which the GAs procedure can be run on several connected processors simultaneously.

  6. Computational Integration of Human Genetic and Toxicological Data to Evaluate AOP-Specific Susceptibility

    Science.gov (United States)

    Susceptibility to environmental chemicals can be modulated by genetic differences. Direct estimation of the genetic contribution to variability in susceptibility to environmental chemicals is only possible in special cases where there is an observed association between exposure a...

  7. Academic training: From Evolution Theory to Parallel and Distributed Genetic Programming

    CERN Multimedia

    2007-01-01

    2006-2007 ACADEMIC TRAINING PROGRAMME LECTURE SERIES 15, 16 March From 11:00 to 12:00 - Main Auditorium, bldg. 500 From Evolution Theory to Parallel and Distributed Genetic Programming F. FERNANDEZ DE VEGA / Univ. of Extremadura, SP Lecture No. 1: From Evolution Theory to Evolutionary Computation Evolutionary computation is a subfield of artificial intelligence (more particularly computational intelligence) involving combinatorial optimization problems, which are based to some degree on the evolution of biological life in the natural world. In this tutorial we will review the source of inspiration for this metaheuristic and its capability for solving problems. We will show the main flavours within the field, and different problems that have been successfully solved employing this kind of techniques. Lecture No. 2: Parallel and Distributed Genetic Programming The successful application of Genetic Programming (GP, one of the available Evolutionary Algorithms) to optimization problems has encouraged an ...

  8. Facial averageness and genetic quality: Testing heritability, genetic correlation with attractiveness, and the paternal age effect.

    Science.gov (United States)

    Lee, Anthony J; Mitchem, Dorian G; Wright, Margaret J; Martin, Nicholas G; Keller, Matthew C; Zietsch, Brendan P

    2016-01-01

    Popular theory suggests that facial averageness is preferred in a partner for genetic benefits to offspring. However, whether facial averageness is associated with genetic quality is yet to be established. Here, we computed an objective measure of facial averageness for a large sample ( N = 1,823) of identical and nonidentical twins and their siblings to test two predictions from the theory that facial averageness reflects genetic quality. First, we use biometrical modelling to estimate the heritability of facial averageness, which is necessary if it reflects genetic quality. We also test for a genetic association between facial averageness and facial attractiveness. Second, we assess whether paternal age at conception (a proxy of mutation load) is associated with facial averageness and facial attractiveness. Our findings are mixed with respect to our hypotheses. While we found that facial averageness does have a genetic component, and a significant phenotypic correlation exists between facial averageness and attractiveness, we did not find a genetic correlation between facial averageness and attractiveness (therefore, we cannot say that the genes that affect facial averageness also affect facial attractiveness) and paternal age at conception was not negatively associated with facial averageness. These findings support some of the previously untested assumptions of the 'genetic benefits' account of facial averageness, but cast doubt on others.

  9. Find the weakest link. A comparison between demographic, genetic and demo-genetic metapopulation extinction times

    Directory of Open Access Journals (Sweden)

    Robert Alexandre

    2011-09-01

    Full Text Available Abstract Background While the ultimate causes of most species extinctions are environmental, environmental constraints have various secondary consequences on evolutionary and ecological processes. The roles of demographic, genetic mechanisms and their interactions in limiting the viabilities of species or populations have stirred much debate and remain difficult to evaluate in the absence of demography-genetics conceptual and technical framework. Here, I computed projected times to metapopulation extinction using (1 a model focusing on the effects of species properties, habitat quality, quantity and temporal variability on the time to demographic extinction; (2 a genetic model focusing on the dynamics of the drift and inbreeding loads under the same species and habitat constraints; (3 a demo-genetic model accounting for demographic-genetic processes and feedbacks. Results Results indicate that a given population may have a high demographic, but low genetic viability or vice versa; and whether genetic or demographic aspects will be the most limiting to overall viability depends on the constraints faced by the species (e.g., reduction of habitat quantity or quality. As a consequence, depending on metapopulation or species characteristics, incorporating genetic considerations to demographically-based viability assessments may either moderately or severely reduce the persistence time. On the other hand, purely genetically-based estimates of species viability may either underestimate (by neglecting demo-genetic interactions or overestimate (by neglecting the demographic resilience true viability. Conclusion Unbiased assessments of the viabilities of species may only be obtained by identifying and considering the most limiting processes (i.e., demography or genetics, or, preferentially, by integrating them.

  10. Semi-automatic classification of skeletal morphology in genetically altered mice using flat-panel volume computed tomography.

    Directory of Open Access Journals (Sweden)

    Christian Dullin

    2007-07-01

    Full Text Available Rapid progress in exploring the human and mouse genome has resulted in the generation of a multitude of mouse models to study gene functions in their biological context. However, effective screening methods that allow rapid noninvasive phenotyping of transgenic and knockout mice are still lacking. To identify murine models with bone alterations in vivo, we used flat-panel volume computed tomography (fpVCT for high-resolution 3-D imaging and developed an algorithm with a computational intelligence system. First, we tested the accuracy and reliability of this approach by imaging discoidin domain receptor 2- (DDR2- deficient mice, which display distinct skull abnormalities as shown by comparative landmark-based analysis. High-contrast fpVCT data of the skull with 200 microm isotropic resolution and 8-s scan time allowed segmentation and computation of significant shape features as well as visualization of morphological differences. The application of a trained artificial neuronal network to these datasets permitted a semi-automatic and highly accurate phenotype classification of DDR2-deficient compared to C57BL/6 wild-type mice. Even heterozygous DDR2 mice with only subtle phenotypic alterations were correctly determined by fpVCT imaging and identified as a new class. In addition, we successfully applied the algorithm to classify knockout mice lacking the DDR1 gene with no apparent skull deformities. Thus, this new method seems to be a potential tool to identify novel mouse phenotypes with skull changes from transgenic and knockout mice on the basis of random mutagenesis as well as from genetic models. However for this purpose, new neuronal networks have to be created and trained. In summary, the combination of fpVCT images with artificial neuronal networks provides a reliable, novel method for rapid, cost-effective, and noninvasive primary screening tool to detect skeletal phenotypes in mice.

  11. [Genetic aspects of genealogy].

    Science.gov (United States)

    Tetushkin, E Iu

    2011-11-01

    The supplementary historical discipline genealogy is also a supplementary genetic discipline. In its formation, genetics borrowed from genealogy some methods of pedigree analysis. In the 21th century, it started receiving contribution from computer-aided genealogy and genetic (molecular) genealogy. The former provides novel tools for genetics, while the latter, which employing genetic methods, enriches genetics with new evidence. Genealogists formulated three main laws ofgenealogy: the law of three generations, the law of doubling the ancestry number, and the law of declining ancestry. The significance and meaning of these laws can be fully understood only in light of genetics. For instance, a controversy between the exponential growth of the number of ancestors of an individual, i.e., the law of doubling the ancestry number, and the limited number of the humankind is explained by the presence of weak inbreeding because of sibs' interference; the latter causes the pedigrees' collapse, i.e., explains also the law of diminishing ancestry number. Mathematic modeling of pedigrees' collapse presented in a number of studies showed that the number of ancestors of each individual attains maximum in a particular generation termed ancestry saturated generation. All representatives of this and preceding generation that left progeny are common ancestors of all current members of the population. In subdivided populations, these generations are more ancient than in panmictic ones, whereas in small isolates and social strata with limited numbers of partners, they are younger. The genealogical law of three generations, according to which each hundred years contain on average three generation intervals, holds for generation lengths for Y-chromosomal DNA, typically equal to 31-32 years; for autosomal and mtDNA, this time is somewhat shorter. Moving along ascending lineas, the number of genetically effective ancestors transmitting their DNA fragment to descendants increases far

  12. COMPUTATIONAL MODELS FOR SUSTAINABLE DEVELOPMENT

    OpenAIRE

    Monendra Grover; Rajesh Kumar; Tapan Kumar Mondal; S. Rajkumar

    2011-01-01

    Genetic erosion is a serious problem and computational models have been developed to prevent it. The computational modeling in this field not only includes (terrestrial) reserve design, but also decision modeling for related problems such as habitat restoration, marine reserve design, and nonreserve approaches to conservation management. Models have been formulated for evaluating tradeoffs between socioeconomic, biophysical, and spatial criteria in establishing marine reserves. The percolatio...

  13. From evolution theory to parallel and distributed genetic

    CERN Multimedia

    CERN. Geneva

    2007-01-01

    Lecture #1: From Evolution Theory to Evolutionary Computation. Evolutionary computation is a subfield of artificial intelligence (more particularly computational intelligence) involving combinatorial optimization problems, which are based to some degree on the evolution of biological life in the natural world. In this tutorial we will review the source of inspiration for this metaheuristic and its capability for solving problems. We will show the main flavours within the field, and different problems that have been successfully solved employing this kind of techniques. Lecture #2: Parallel and Distributed Genetic Programming. The successful application of Genetic Programming (GP, one of the available Evolutionary Algorithms) to optimization problems has encouraged an increasing number of researchers to apply these techniques to a large set of problems. Given the difficulty of some problems, much effort has been applied to improving the efficiency of GP during the last few years. Among the available proposals,...

  14. Identical twins in forensic genetics

    DEFF Research Database (Denmark)

    Tvedebrink, Torben; Morling, Niels

    2015-01-01

    The increase in the number of forensic genetic loci used for identification purposes results in infinitesimal random match probabilities. These probabilities are computed under assumptions made for rather simple population genetic models. Often, the forensic expert reports likelihood ratios, where...... published results accounting for close familial relationships. However, we revisit the discussion to increase the awareness among forensic genetic practitioners and include new information on medical and societal factors to assess the risk of not considering a monozygotic twin as the true perpetrator......, then data relevant for the Danish society suggests that the threshold of likelihood ratios should approximately be between 150,000 and 2,000,000 in order to take the risk of an unrecognised identical, monozygotic twin into consideration. In other societies, the threshold of the likelihood ratio in crime...

  15. Predictability of Genetic Interactions from Functional Gene Modules

    Directory of Open Access Journals (Sweden)

    Jonathan H. Young

    2017-02-01

    Full Text Available Characterizing genetic interactions is crucial to understanding cellular and organismal response to gene-level perturbations. Such knowledge can inform the selection of candidate disease therapy targets, yet experimentally determining whether genes interact is technically nontrivial and time-consuming. High-fidelity prediction of different classes of genetic interactions in multiple organisms would substantially alleviate this experimental burden. Under the hypothesis that functionally related genes tend to share common genetic interaction partners, we evaluate a computational approach to predict genetic interactions in Homo sapiens, Drosophila melanogaster, and Saccharomyces cerevisiae. By leveraging knowledge of functional relationships between genes, we cross-validate predictions on known genetic interactions and observe high predictive power of multiple classes of genetic interactions in all three organisms. Additionally, our method suggests high-confidence candidate interaction pairs that can be directly experimentally tested. A web application is provided for users to query genes for predicted novel genetic interaction partners. Finally, by subsampling the known yeast genetic interaction network, we found that novel genetic interactions are predictable even when knowledge of currently known interactions is minimal.

  16. Applications of Evolutionary Computation

    NARCIS (Netherlands)

    Mora, Antonio M.; Squillero, Giovanni; Di Chio, C; Agapitos, Alexandros; Cagnoni, Stefano; Cotta, Carlos; Fernández De Vega, F; Di Caro, G A; Drechsler, R.; Ekárt, A; Esparcia-Alcázar, Anna I.; Farooq, M; Langdon, W B; Merelo-Guervós, J.J.; Preuss, M; Richter, O.-M.H.; Silva, Sara; Sim$\\$~oes, A; Squillero, Giovanni; Tarantino, Ernesto; Tettamanzi, Andrea G B; Togelius, J; Urquhart, Neil; Uyar, A S; Yannakakis, G N; Smith, Stephen L; Caserta, Marco; Ramirez, Adriana; Voß, Stefan; Squillero, Giovanni; Burelli, Paolo; Mora, Antonio M.; Squillero, Giovanni; Jan, Mathieu; Matthias, M; Di Chio, C; Agapitos, Alexandros; Cagnoni, Stefano; Cotta, Carlos; Fernández De Vega, F; Di Caro, G A; Drechsler, R.; Ekárt, A; Esparcia-Alcázar, Anna I.; Farooq, M; Langdon, W B; Merelo-Guervós, J.J.; Preuss, M; Richter, O.-M.H.; Silva, Sara; Sim$\\$~oes, A; Squillero, Giovanni; Tarantino, Ernesto; Tettamanzi, Andrea G B; Togelius, J; Urquhart, Neil; Uyar, A S; Yannakakis, G N; Caserta, Marco; Ramirez, Adriana; Voß, Stefan; Squillero, Giovanni; Burelli, Paolo; Esparcia-Alcazar, Anna I; Silva, Sara; Agapitos, Alexandros; Cotta, Carlos; De Falco, Ivanoe; Cioppa, Antonio Della; Diwold, Konrad; Ekart, Aniko; Tarantino, Ernesto; Vega, Francisco Fernandez De; Burelli, Paolo; Sim, Kevin; Cagnoni, Stefano; Simoes, Anabela; Merelo, J.J.; Urquhart, Neil; Haasdijk, Evert; Zhang, Mengjie; Squillero, Giovanni; Eiben, A E; Tettamanzi, Andrea G B; Glette, Kyrre; Rohlfshagen, Philipp; Schaefer, Robert; Caserta, Marco; Ramirez, Adriana; Voß, Stefan

    2015-01-01

    The application of genetic and evolutionary computation to problems in medicine has increased rapidly over the past five years, but there are specific issues and challenges that distinguish it from other real-world applications. Obtaining reliable and coherent patient data, establishing the clinical

  17. Multiple-trait genetic evaluation using genomic matrix

    African Journals Online (AJOL)

    Jane

    2011-07-06

    Jul 6, 2011 ... relationships was estimated through computer simulation and was compared with the accuracy of ... programs, detect animals with superior genetic and select ... genomic matrices in the mixed model equations of BLUP.

  18. The UK Human Genome Mapping Project online computing service.

    Science.gov (United States)

    Rysavy, F R; Bishop, M J; Gibbs, G P; Williams, G W

    1992-04-01

    This paper presents an overview of computing and networking facilities developed by the Medical Research Council to provide online computing support to the Human Genome Mapping Project (HGMP) in the UK. The facility is connected to a number of other computing facilities in various centres of genetics and molecular biology research excellence, either directly via high-speed links or through national and international wide-area networks. The paper describes the design and implementation of the current system, a 'client/server' network of Sun, IBM, DEC and Apple servers, gateways and workstations. A short outline of online computing services currently delivered by this system to the UK human genetics research community is also provided. More information about the services and their availability could be obtained by a direct approach to the UK HGMP-RC.

  19. Analysis of genetic polymorphism of nine short tandem repeat loci in ...

    African Journals Online (AJOL)

    Yomi

    2012-03-15

    Mar 15, 2012 ... Key words: short tandem repeat, repeat motif, genetic polymorphism, Han population, forensic genetics. INTRODUCTION. Short tandem repeat (STR) is widely .... Data analysis. The exact test of Hardy-Weinberg equilibrium was conducted with. Arlequin version 3.5 software (Computational and Molecular.

  20. Resizing Technique-Based Hybrid Genetic Algorithm for Optimal Drift Design of Multistory Steel Frame Buildings

    Directory of Open Access Journals (Sweden)

    Hyo Seon Park

    2014-01-01

    Full Text Available Since genetic algorithm-based optimization methods are computationally expensive for practical use in the field of structural optimization, a resizing technique-based hybrid genetic algorithm for the drift design of multistory steel frame buildings is proposed to increase the convergence speed of genetic algorithms. To reduce the number of structural analyses required for the convergence, a genetic algorithm is combined with a resizing technique that is an efficient optimal technique to control the drift of buildings without the repetitive structural analysis. The resizing technique-based hybrid genetic algorithm proposed in this paper is applied to the minimum weight design of three steel frame buildings. To evaluate the performance of the algorithm, optimum weights, computational times, and generation numbers from the proposed algorithm are compared with those from a genetic algorithm. Based on the comparisons, it is concluded that the hybrid genetic algorithm shows clear improvements in convergence properties.

  1. Extraordinarily Adaptive Properties of the Genetically Encoded Amino Acids

    Science.gov (United States)

    Ilardo, Melissa; Meringer, Markus; Freeland, Stephen; Rasulev, Bakhtiyor; Cleaves II, H. James

    2015-01-01

    Using novel advances in computational chemistry, we demonstrate that the set of 20 genetically encoded amino acids, used nearly universally to construct all coded terrestrial proteins, has been highly influenced by natural selection. We defined an adaptive set of amino acids as one whose members thoroughly cover relevant physico-chemical properties, or “chemistry space.” Using this metric, we compared the encoded amino acid alphabet to random sets of amino acids. These random sets were drawn from a computationally generated compound library containing 1913 alternative amino acids that lie within the molecular weight range of the encoded amino acids. Sets that cover chemistry space better than the genetically encoded alphabet are extremely rare and energetically costly. Further analysis of more adaptive sets reveals common features and anomalies, and we explore their implications for synthetic biology. We present these computations as evidence that the set of 20 amino acids found within the standard genetic code is the result of considerable natural selection. The amino acids used for constructing coded proteins may represent a largely global optimum, such that any aqueous biochemistry would use a very similar set. PMID:25802223

  2. Extraordinarily adaptive properties of the genetically encoded amino acids.

    Science.gov (United States)

    Ilardo, Melissa; Meringer, Markus; Freeland, Stephen; Rasulev, Bakhtiyor; Cleaves, H James

    2015-03-24

    Using novel advances in computational chemistry, we demonstrate that the set of 20 genetically encoded amino acids, used nearly universally to construct all coded terrestrial proteins, has been highly influenced by natural selection. We defined an adaptive set of amino acids as one whose members thoroughly cover relevant physico-chemical properties, or "chemistry space." Using this metric, we compared the encoded amino acid alphabet to random sets of amino acids. These random sets were drawn from a computationally generated compound library containing 1913 alternative amino acids that lie within the molecular weight range of the encoded amino acids. Sets that cover chemistry space better than the genetically encoded alphabet are extremely rare and energetically costly. Further analysis of more adaptive sets reveals common features and anomalies, and we explore their implications for synthetic biology. We present these computations as evidence that the set of 20 amino acids found within the standard genetic code is the result of considerable natural selection. The amino acids used for constructing coded proteins may represent a largely global optimum, such that any aqueous biochemistry would use a very similar set.

  3. A Parallel Genetic Algorithm for Automated Electronic Circuit Design

    Science.gov (United States)

    Long, Jason D.; Colombano, Silvano P.; Haith, Gary L.; Stassinopoulos, Dimitris

    2000-01-01

    Parallelized versions of genetic algorithms (GAs) are popular primarily for three reasons: the GA is an inherently parallel algorithm, typical GA applications are very compute intensive, and powerful computing platforms, especially Beowulf-style computing clusters, are becoming more affordable and easier to implement. In addition, the low communication bandwidth required allows the use of inexpensive networking hardware such as standard office ethernet. In this paper we describe a parallel GA and its use in automated high-level circuit design. Genetic algorithms are a type of trial-and-error search technique that are guided by principles of Darwinian evolution. Just as the genetic material of two living organisms can intermix to produce offspring that are better adapted to their environment, GAs expose genetic material, frequently strings of 1s and Os, to the forces of artificial evolution: selection, mutation, recombination, etc. GAs start with a pool of randomly-generated candidate solutions which are then tested and scored with respect to their utility. Solutions are then bred by probabilistically selecting high quality parents and recombining their genetic representations to produce offspring solutions. Offspring are typically subjected to a small amount of random mutation. After a pool of offspring is produced, this process iterates until a satisfactory solution is found or an iteration limit is reached. Genetic algorithms have been applied to a wide variety of problems in many fields, including chemistry, biology, and many engineering disciplines. There are many styles of parallelism used in implementing parallel GAs. One such method is called the master-slave or processor farm approach. In this technique, slave nodes are used solely to compute fitness evaluations (the most time consuming part). The master processor collects fitness scores from the nodes and performs the genetic operators (selection, reproduction, variation, etc.). Because of dependency

  4. High School Students' Use of Meiosis When Solving Genetics Problems.

    Science.gov (United States)

    Wynne, Cynthia F.; Stewart, Jim; Passmore, Cindy

    2001-01-01

    Paints a different picture of students' reasoning with meiosis as they solved complex, computer-generated genetics problems, some of which required them to revise their understanding of meiosis in response to anomalous data. Students were able to develop a rich understanding of meiosis and can utilize that knowledge to solve genetics problems.…

  5. Complexity theory and genetics: The computational power of crossing over

    Czech Academy of Sciences Publication Activity Database

    Pudlák, Pavel

    2001-01-01

    Roč. 171, č. 1 (2001), s. 201-223 ISSN 0890-5401 R&D Projects: GA AV ČR IAA1019901 Institutional research plan: CEZ:AV0Z1019905; CEZ:AV0Z1019905 Keywords : complexity * genetics * croning over Subject RIV: BA - General Mathematics Impact factor: 0.571, year: 2001

  6. Computational challenges in modeling gene regulatory events.

    Science.gov (United States)

    Pataskar, Abhijeet; Tiwari, Vijay K

    2016-10-19

    Cellular transcriptional programs driven by genetic and epigenetic mechanisms could be better understood by integrating "omics" data and subsequently modeling the gene-regulatory events. Toward this end, computational biology should keep pace with evolving experimental procedures and data availability. This article gives an exemplified account of the current computational challenges in molecular biology.

  7. Computational methods in metabolic engineering for strain design.

    Science.gov (United States)

    Long, Matthew R; Ong, Wai Kit; Reed, Jennifer L

    2015-08-01

    Metabolic engineering uses genetic approaches to control microbial metabolism to produce desired compounds. Computational tools can identify new biological routes to chemicals and the changes needed in host metabolism to improve chemical production. Recent computational efforts have focused on exploring what compounds can be made biologically using native, heterologous, and/or enzymes with broad specificity. Additionally, computational methods have been developed to suggest different types of genetic modifications (e.g. gene deletion/addition or up/down regulation), as well as suggest strategies meeting different criteria (e.g. high yield, high productivity, or substrate co-utilization). Strategies to improve the runtime performances have also been developed, which allow for more complex metabolic engineering strategies to be identified. Future incorporation of kinetic considerations will further improve strain design algorithms. Copyright © 2015 Elsevier Ltd. All rights reserved.

  8. The Genetic Activity Profile database.

    Science.gov (United States)

    Waters, M D; Stack, H F; Garrett, N E; Jackson, M A

    1991-12-01

    A graphic approach termed a Genetic Activity Profile (GAP) has been developed to display a matrix of data on the genetic and related effects of selected chemical agents. The profiles provide a visual overview of the quantitative (doses) and qualitative (test results) data for each chemical. Either the lowest effective dose (LED) or highest ineffective dose (HID) is recorded for each agent and bioassay. Up to 200 different test systems are represented across the GAP. Bioassay systems are organized according to the phylogeny of the test organisms and the end points of genetic activity. The methodology for the production and evaluation of GAPs has been developed in collaboration with the International Agency for Research on Cancer. Data on individual chemicals have been compiled by IARC and by the U.S. Environmental Protection Agency. Data are available on 299 compounds selected from volumes 1-50 of the IARC Monographs and on 115 compounds identified as Superfund Priority Substances. Software to display the GAPs on an IBM-compatible personal computer is available from the authors. Structurally similar compounds frequently display qualitatively and quantitatively similar GAPs. By examining the patterns of GAPs of pairs and groups of chemicals, it is possible to make more informed decisions regarding the selection of test batteries to be used in evaluating chemical analogs. GAPs have provided useful data for the development of weight-of-evidence hazard ranking schemes. Also, some knowledge of the potential genetic activity of complex environmental mixtures may be gained from assessing the GAPs of component chemicals. The fundamental techniques and computer programs devised for the GAP database may be used to develop similar databases in other disciplines.

  9. Selection on Optimal Haploid Value Increases Genetic Gain and Preserves More Genetic Diversity Relative to Genomic Selection.

    Science.gov (United States)

    Daetwyler, Hans D; Hayden, Matthew J; Spangenberg, German C; Hayes, Ben J

    2015-08-01

    Doubled haploids are routinely created and phenotypically selected in plant breeding programs to accelerate the breeding cycle. Genomic selection, which makes use of both phenotypes and genotypes, has been shown to further improve genetic gain through prediction of performance before or without phenotypic characterization of novel germplasm. Additional opportunities exist to combine genomic prediction methods with the creation of doubled haploids. Here we propose an extension to genomic selection, optimal haploid value (OHV) selection, which predicts the best doubled haploid that can be produced from a segregating plant. This method focuses selection on the haplotype and optimizes the breeding program toward its end goal of generating an elite fixed line. We rigorously tested OHV selection breeding programs, using computer simulation, and show that it results in up to 0.6 standard deviations more genetic gain than genomic selection. At the same time, OHV selection preserved a substantially greater amount of genetic diversity in the population than genomic selection, which is important to achieve long-term genetic gain in breeding populations. Copyright © 2015 by the Genetics Society of America.

  10. A hybrid niched-island genetic algorithm applied to a nuclear core optimization problem

    International Nuclear Information System (INIS)

    Pereira, Claudio M.N.A.

    2005-01-01

    Diversity maintenance is a key-feature in most genetic-based optimization processes. The quest for such characteristic, has been motivating improvements in the original genetic algorithm (GA). The use of multiple populations (called islands) has demonstrating to increase diversity, delaying the genetic drift. Island Genetic Algorithms (IGA) lead to better results, however, the drift is only delayed, but not avoided. An important advantage of this approach is the simplicity and efficiency for parallel processing. Diversity can also be improved by the use of niching techniques. Niched Genetic Algorithms (NGA) are able to avoid the genetic drift, by containing evolution in niches of a single-population GA, however computational cost is increased. In this work it is investigated the use of a hybrid Niched-Island Genetic Algorithm (NIGA) in a nuclear core optimization problem found in literature. Computational experiments demonstrate that it is possible to take advantage of both, performance enhancement due to the parallelism and drift avoidance due to the use of niches. Comparative results shown that the proposed NIGA demonstrated to be more efficient and robust than an IGA and a NGA for solving the proposed optimization problem. (author)

  11. Supervised Machine Learning for Population Genetics: A New Paradigm

    Science.gov (United States)

    Schrider, Daniel R.; Kern, Andrew D.

    2018-01-01

    As population genomic datasets grow in size, researchers are faced with the daunting task of making sense of a flood of information. To keep pace with this explosion of data, computational methodologies for population genetic inference are rapidly being developed to best utilize genomic sequence data. In this review we discuss a new paradigm that has emerged in computational population genomics: that of supervised machine learning (ML). We review the fundamentals of ML, discuss recent applications of supervised ML to population genetics that outperform competing methods, and describe promising future directions in this area. Ultimately, we argue that supervised ML is an important and underutilized tool that has considerable potential for the world of evolutionary genomics. PMID:29331490

  12. A primer on high-throughput computing for genomic selection.

    Science.gov (United States)

    Wu, Xiao-Lin; Beissinger, Timothy M; Bauck, Stewart; Woodward, Brent; Rosa, Guilherme J M; Weigel, Kent A; Gatti, Natalia de Leon; Gianola, Daniel

    2011-01-01

    High-throughput computing (HTC) uses computer clusters to solve advanced computational problems, with the goal of accomplishing high-throughput over relatively long periods of time. In genomic selection, for example, a set of markers covering the entire genome is used to train a model based on known data, and the resulting model is used to predict the genetic merit of selection candidates. Sophisticated models are very computationally demanding and, with several traits to be evaluated sequentially, computing time is long, and output is low. In this paper, we present scenarios and basic principles of how HTC can be used in genomic selection, implemented using various techniques from simple batch processing to pipelining in distributed computer clusters. Various scripting languages, such as shell scripting, Perl, and R, are also very useful to devise pipelines. By pipelining, we can reduce total computing time and consequently increase throughput. In comparison to the traditional data processing pipeline residing on the central processors, performing general-purpose computation on a graphics processing unit provide a new-generation approach to massive parallel computing in genomic selection. While the concept of HTC may still be new to many researchers in animal breeding, plant breeding, and genetics, HTC infrastructures have already been built in many institutions, such as the University of Wisconsin-Madison, which can be leveraged for genomic selection, in terms of central processing unit capacity, network connectivity, storage availability, and middleware connectivity. Exploring existing HTC infrastructures as well as general-purpose computing environments will further expand our capability to meet increasing computing demands posed by unprecedented genomic data that we have today. We anticipate that HTC will impact genomic selection via better statistical models, faster solutions, and more competitive products (e.g., from design of marker panels to realized

  13. Fine-Scale Genetic Structure in Finland

    Directory of Open Access Journals (Sweden)

    Sini Kerminen

    2017-10-01

    Full Text Available Coupling dense genotype data with new computational methods offers unprecedented opportunities for individual-level ancestry estimation once geographically precisely defined reference data sets become available. We study such a reference data set for Finland containing 2376 such individuals from the FINRISK Study survey of 1997 both of whose parents were born close to each other. This sampling strategy focuses on the population structure present in Finland before the 1950s. By using the recent haplotype-based methods ChromoPainter (CP and FineSTRUCTURE (FS we reveal a highly geographically clustered genetic structure in Finland and report its connections to the settlement history as well as to the current dialectal regions of the Finnish language. The main genetic division within Finland shows striking concordance with the 1323 borderline of the treaty of Nöteborg. In general, we detect genetic substructure throughout the country, which reflects stronger regional genetic differences in Finland compared to, for example, the UK, which in a similar analysis was dominated by a single unstructured population. We expect that similar population genetic reference data sets will become available for many more populations in the near future with important applications, for example, in forensic genetics and in genetic association studies. With this in mind, we report those extensions of the CP + FS approach that we found most useful in our analyses of the Finnish data.

  14. Recent developments in computer modeling add ecological realism to landscape genetics

    Science.gov (United States)

    Background / Question / Methods A factor limiting the rate of progress in landscape genetics has been the shortage of spatial models capable of linking life history attributes such as dispersal behavior to complex dynamic landscape features. The recent development of new models...

  15. DNAStat, version 2.1--a computer program for processing genetic profile databases and biostatistical calculations.

    Science.gov (United States)

    Berent, Jarosław

    2010-01-01

    This paper presents the new DNAStat version 2.1 for processing genetic profile databases and biostatistical calculations. The popularization of DNA studies employed in the judicial system has led to the necessity of developing appropriate computer programs. Such programs must, above all, address two critical problems, i.e. the broadly understood data processing and data storage, and biostatistical calculations. Moreover, in case of terrorist attacks and mass natural disasters, the ability to identify victims by searching related individuals is very important. DNAStat version 2.1 is an adequate program for such purposes. The DNAStat version 1.0 was launched in 2005. In 2006, the program was updated to 1.1 and 1.2 versions. There were, however, slight differences between those versions and the original one. The DNAStat version 2.0 was launched in 2007 and the major program improvement was an introduction of the group calculation options with the potential application to personal identification of mass disasters and terrorism victims. The last 2.1 version has the option of language selection--Polish or English, which will enhance the usage and application of the program also in other countries.

  16. Decision optimization of case-based computer-aided decision systems using genetic algorithms with application to mammography

    International Nuclear Information System (INIS)

    Mazurowski, Maciej A; Habas, Piotr A; Zurada, Jacek M; Tourassi, Georgia D

    2008-01-01

    This paper presents an optimization framework for improving case-based computer-aided decision (CB-CAD) systems. The underlying hypothesis of the study is that each example in the knowledge database of a medical decision support system has different importance in the decision making process. A new decision algorithm incorporating an importance weight for each example is proposed to account for these differences. The search for the best set of importance weights is defined as an optimization problem and a genetic algorithm is employed to solve it. The optimization process is tailored to maximize the system's performance according to clinically relevant evaluation criteria. The study was performed using a CAD system developed for the classification of regions of interests (ROIs) in mammograms as depicting masses or normal tissue. The system was constructed and evaluated using a dataset of ROIs extracted from the Digital Database for Screening Mammography (DDSM). Experimental results show that, according to receiver operator characteristic (ROC) analysis, the proposed method significantly improves the overall performance of the CAD system as well as its average specificity for high breast mass detection rates

  17. Computer-aided design for metabolic engineering.

    Science.gov (United States)

    Fernández-Castané, Alfred; Fehér, Tamás; Carbonell, Pablo; Pauthenier, Cyrille; Faulon, Jean-Loup

    2014-12-20

    The development and application of biotechnology-based strategies has had a great socio-economical impact and is likely to play a crucial role in the foundation of more sustainable and efficient industrial processes. Within biotechnology, metabolic engineering aims at the directed improvement of cellular properties, often with the goal of synthesizing a target chemical compound. The use of computer-aided design (CAD) tools, along with the continuously emerging advanced genetic engineering techniques have allowed metabolic engineering to broaden and streamline the process of heterologous compound-production. In this work, we review the CAD tools available for metabolic engineering with an emphasis, on retrosynthesis methodologies. Recent advances in genetic engineering strategies for pathway implementation and optimization are also reviewed as well as a range of bionalytical tools to validate in silico predictions. A case study applying retrosynthesis is presented as an experimental verification of the output from Retropath, the first complete automated computational pipeline applicable to metabolic engineering. Applying this CAD pipeline, together with genetic reassembly and optimization of culture conditions led to improved production of the plant flavonoid pinocembrin. Coupling CAD tools with advanced genetic engineering strategies and bioprocess optimization is crucial for enhanced product yields and will be of great value for the development of non-natural products through sustainable biotechnological processes. Copyright © 2014 Elsevier B.V. All rights reserved.

  18. Integration of genetic algorithm, computer simulation and design of experiments for forecasting electrical energy consumption

    International Nuclear Information System (INIS)

    Azadeh, A.; Tarverdian, S.

    2007-01-01

    This study presents an integrated algorithm for forecasting monthly electrical energy consumption based on genetic algorithm (GA), computer simulation and design of experiments using stochastic procedures. First, time-series model is developed as a benchmark for GA and simulation. Computer simulation is developed to generate random variables for monthly electricity consumption. This is achieved to foresee the effects of probabilistic distribution on monthly electricity consumption. The GA and simulated-based GA models are then developed by the selected time-series model. Therefore, there are four treatments to be considered in analysis of variance (ANOVA) which are actual data, time series, GA and simulated-based GA. Furthermore, ANOVA is used to test the null hypothesis of the above four alternatives being equal. If the null hypothesis is accepted, then the lowest mean absolute percentage error (MAPE) value is used to select the best model, otherwise the Duncan Multiple Range Test (DMRT) method of paired comparison is used to select the optimum model, which could be time series, GA or simulated-based GA. In case of ties the lowest MAPE value is considered as the benchmark. The integrated algorithm has several unique features. First, it is flexible and identifies the best model based on the results of ANOVA and MAPE, whereas previous studies consider the best-fit GA model based on MAPE or relative error results. Second, the proposed algorithm may identify conventional time series as the best model for future electricity consumption forecasting because of its dynamic structure, whereas previous studies assume that GA always provide the best solutions and estimation. To show the applicability and superiority of the proposed algorithm, the monthly electricity consumption in Iran from March 1994 to February 2005 (131 months) is used and applied to the proposed algorithm

  19. GPGPU Implementation of a Genetic Algorithm for Stereo Refinement

    Directory of Open Access Journals (Sweden)

    Álvaro Arranz

    2015-03-01

    Full Text Available During the last decade, the general-purpose computing on graphics processing units Graphics (GPGPU has turned out to be a useful tool for speeding up many scientific calculations. Computer vision is known to be one of the fields with more penetration of these new techniques. This paper explores the advantages of using GPGPU implementation to speedup a genetic algorithm used for stereo refinement. The main contribution of this paper is analyzing which genetic operators take advantage of a parallel approach and the description of an efficient state- of-the-art implementation for each one. As a result, speed-ups close to x80 can be achieved, demonstrating to be the only way of achieving close to real-time performance.

  20. Computer science and operations research

    CERN Document Server

    Balci, Osman

    1992-01-01

    The interface of Operation Research and Computer Science - although elusive to a precise definition - has been a fertile area of both methodological and applied research. The papers in this book, written by experts in their respective fields, convey the current state-of-the-art in this interface across a broad spectrum of research domains which include optimization techniques, linear programming, interior point algorithms, networks, computer graphics in operations research, parallel algorithms and implementations, planning and scheduling, genetic algorithms, heuristic search techniques and dat

  1. Application of Genetic Algorithms in Seismic Tomography

    Science.gov (United States)

    Soupios, Pantelis; Akca, Irfan; Mpogiatzis, Petros; Basokur, Ahmet; Papazachos, Constantinos

    2010-05-01

    In the earth sciences several inverse problems that require data fitting and parameter estimation are nonlinear and can involve a large number of unknown parameters. Consequently, the application of analytical inversion or optimization techniques may be quite restrictive. In practice, most analytical methods are local in nature and rely on a linearized form of the problem in question, adopting an iterative procedure using partial derivatives to improve an initial model. This approach can lead to a dependence of the final model solution on the starting model and is prone to entrapment in local misfit minima. Moreover, the calculation of derivatives can be computationally inefficient and create instabilities when numerical approximations are used. In contrast to these local minimization methods, global techniques that do not rely on partial derivatives, are independent of the form of the data misfit criterion, and are computationally robust. Such methods often use random processes to sample a selected wider span of the model space. In this situation, randomly generated models are assessed in terms of their data-fitting quality and the process may be stopped after a certain number of acceptable models is identified or continued until a satisfactory data fit is achieved. A new class of methods known as genetic algorithms achieves the aforementioned approximation through novel model representation and manipulations. Genetic algorithms (GAs) were originally developed in the field of artificial intelligence by John Holland more than 20 years ago, but even in this field it is less than a decade that the methodology has been more generally applied and only recently did the methodology attract the attention of the earth sciences community. Applications have been generally concentrated in geophysics and in particular seismology. As awareness of genetic algorithms grows there surely will be many more and varied applications to earth science problems. In the present work, the

  2. Soft computing techniques in engineering applications

    CERN Document Server

    Zhong, Baojiang

    2014-01-01

    The Soft Computing techniques, which are based on the information processing of biological systems are now massively used in the area of pattern recognition, making prediction & planning, as well as acting on the environment. Ideally speaking, soft computing is not a subject of homogeneous concepts and techniques; rather, it is an amalgamation of distinct methods that confirms to its guiding principle. At present, the main aim of soft computing is to exploit the tolerance for imprecision and uncertainty to achieve tractability, robustness and low solutions cost. The principal constituents of soft computing techniques are probabilistic reasoning, fuzzy logic, neuro-computing, genetic algorithms, belief networks, chaotic systems, as well as learning theory. This book covers contributions from various authors to demonstrate the use of soft computing techniques in various applications of engineering.  

  3. Sex-specific genetic effects in physical activity: results from a quantitative genetic analysis.

    Science.gov (United States)

    Diego, Vincent P; de Chaves, Raquel Nichele; Blangero, John; de Souza, Michele Caroline; Santos, Daniel; Gomes, Thayse Natacha; dos Santos, Fernanda Karina; Garganta, Rui; Katzmarzyk, Peter T; Maia, José A R

    2015-08-01

    The objective of this study is to present a model to estimate sex-specific genetic effects on physical activity (PA) levels and sedentary behaviour (SB) using three generation families. The sample consisted of 100 families covering three generations from Portugal. PA and SB were assessed via the International Physical Activity Questionnaire short form (IPAQ-SF). Sex-specific effects were assessed by genotype-by-sex interaction (GSI) models and sex-specific heritabilities. GSI effects and heterogeneity were tested in the residual environmental variance. SPSS 17 and SOLAR v. 4.1 were used in all computations. The genetic component for PA and SB domains varied from low to moderate (11% to 46%), when analyzing both genders combined. We found GSI effects for vigorous PA (p = 0.02) and time spent watching television (WT) (p < 0.001) that showed significantly higher additive genetic variance estimates in males. The heterogeneity in the residual environmental variance was significant for moderate PA (p = 0.02), vigorous PA (p = 0.006) and total PA (p = 0.001). Sex-specific heritability estimates were significantly higher in males only for WT, with a male-to-female difference in heritability of 42.5 (95% confidence interval: 6.4, 70.4). Low to moderate genetic effects on PA and SB traits were found. Results from the GSI model show that there are sex-specific effects in two phenotypes, VPA and WT with a stronger genetic influence in males.

  4. An overview on Approximate Bayesian computation*

    Directory of Open Access Journals (Sweden)

    Baragatti Meïli

    2014-01-01

    Full Text Available Approximate Bayesian computation techniques, also called likelihood-free methods, are one of the most satisfactory approach to intractable likelihood problems. This overview presents recent results since its introduction about ten years ago in population genetics.

  5. Modeling the Isentropic Head Value of Centrifugal Gas Compressor using Genetic Programming

    Directory of Open Access Journals (Sweden)

    Safiyullah Ferozkhan

    2016-01-01

    Full Text Available Gas compressor performance is vital in oil and gas industry because of the equipment criticality which requires continuous operations. Plant operators often face difficulties in predicting appropriate time for maintenance and would usually rely on time based predictive maintenance intervals as recommended by original equipment manufacturer (OEM. The objective of this work is to develop the computational model to find the isentropic head value using genetic programming. The isentropic head value is calculated from the OEM performance chart. Inlet mass flow rate and speed of the compressor are taken as the input value. The obtained results from the GP computational models show good agreement with experimental and target data with the average prediction error of 1.318%. The genetic programming computational model will assist machinery engineers to quantify performance deterioration of gas compressor and the results from this study will be then utilized to estimate future maintenance requirements based on the historical data. In general, this genetic programming modelling provides a powerful solution for gas compressor operators to realize predictive maintenance approach in their operations.

  6. Soft Computing Methods in Design of Superalloys

    Science.gov (United States)

    Cios, K. J.; Berke, L.; Vary, A.; Sharma, S.

    1996-01-01

    Soft computing techniques of neural networks and genetic algorithms are used in the design of superalloys. The cyclic oxidation attack parameter K(sub a), generated from tests at NASA Lewis Research Center, is modelled as a function of the superalloy chemistry and test temperature using a neural network. This model is then used in conjunction with a genetic algorithm to obtain an optimized superalloy composition resulting in low K(sub a) values.

  7. ADAM: A computer program to simulate selective-breeding schemes for animals

    DEFF Research Database (Denmark)

    Pedersen, L D; Sørensen, A C; Henryon, M

    2009-01-01

    ADAM is a computer program that models selective breeding schemes for animals using stochastic simulation. The program simulates a population of animals and traces the genetic changes in the population under different selective breeding scenarios. It caters to different population structures......, genetic models, selection strategies, and mating designs. ADAM can be used to evaluate breeding schemes and generate genetic data to test statistical tools...

  8. Rapid genetic algorithm optimization of a mouse computational model: Benefits for anthropomorphization of neonatal mouse cardiomyocytes

    Directory of Open Access Journals (Sweden)

    Corina Teodora Bot

    2012-11-01

    Full Text Available While the mouse presents an invaluable experimental model organism in biology, its usefulness in cardiac arrhythmia research is limited in some aspects due to major electrophysiological differences between murine and human action potentials (APs. As previously described, these species-specific traits can be partly overcome by application of a cell-type transforming clamp (CTC to anthropomorphize the murine cardiac AP. CTC is a hybrid experimental-computational dynamic clamp technique, in which a computationally calculated time-dependent current is inserted into a cell in real time, to compensate for the differences between sarcolemmal currents of that cell (e.g., murine and the desired species (e.g., human. For effective CTC performance, mismatch between the measured cell and a mathematical model used to mimic the measured AP must be minimal. We have developed a genetic algorithm (GA approach that rapidly tunes a mathematical model to reproduce the AP of the murine cardiac myocyte under study. Compared to a prior implementation that used a template-based model selection approach, we show that GA optimization to a cell-specific model results in a much better recapitulation of the desired AP morphology with CTC. This improvement was more pronounced when anthropomorphizing neonatal mouse cardiomyocytes to human-like APs than to guinea pig APs. CTC may be useful for a wide range of applications, from screening effects of pharmaceutical compounds on ion channel activity, to exploring variations in the mouse or human genome. Rapid GA optimization of a cell-specific mathematical model improves CTC performance and may therefore expand the applicability and usage of the CTC technique.

  9. Mission Planning for Unmanned Aircraft with Genetic Algorithms

    DEFF Research Database (Denmark)

    Hansen, Karl Damkjær

    unmanned aircraft are used for aerial surveying of the crops. The farmer takes the role of the analyst above, who does not necessarily have any specific interest in remote controlled aircraft but needs the outcome of the survey. The recurring method in the study is the genetic algorithm; a flexible...... contributions are made in the area of the genetic algorithms. One is a method to decide on the right time to stop the computation of the plan, when the right balance is stricken between using the time planning and using the time flying. The other contribution is a characterization of the evolutionary operators...... used in the genetic algorithm. The result is a measure based on entropy to evaluate and control the diversity of the population of the genetic algorithm, which is an important factor its effectiveness....

  10. Traffic Flow Prediction Model for Large-Scale Road Network Based on Cloud Computing

    Directory of Open Access Journals (Sweden)

    Zhaosheng Yang

    2014-01-01

    Full Text Available To increase the efficiency and precision of large-scale road network traffic flow prediction, a genetic algorithm-support vector machine (GA-SVM model based on cloud computing is proposed in this paper, which is based on the analysis of the characteristics and defects of genetic algorithm and support vector machine. In cloud computing environment, firstly, SVM parameters are optimized by the parallel genetic algorithm, and then this optimized parallel SVM model is used to predict traffic flow. On the basis of the traffic flow data of Haizhu District in Guangzhou City, the proposed model was verified and compared with the serial GA-SVM model and parallel GA-SVM model based on MPI (message passing interface. The results demonstrate that the parallel GA-SVM model based on cloud computing has higher prediction accuracy, shorter running time, and higher speedup.

  11. Analysis of genetic diversity and estimation of inbreeding coefficient ...

    African Journals Online (AJOL)

    STORAGESEVER

    2010-01-18

    Jan 18, 2010 ... Key words: Genetic diversity, microsatellite markers, Caspian horse breed. INTRODUCTION ... heterozygosity, observed and effective number of alleles at each ... computer program version 1.31 (Yeh et al., 1999). Based on ...

  12. Deciphering the genetic regulatory code using an inverse error control coding framework.

    Energy Technology Data Exchange (ETDEWEB)

    Rintoul, Mark Daniel; May, Elebeoba Eni; Brown, William Michael; Johnston, Anna Marie; Watson, Jean-Paul

    2005-03-01

    We have found that developing a computational framework for reconstructing error control codes for engineered data and ultimately for deciphering genetic regulatory coding sequences is a challenging and uncharted area that will require advances in computational technology for exact solutions. Although exact solutions are desired, computational approaches that yield plausible solutions would be considered sufficient as a proof of concept to the feasibility of reverse engineering error control codes and the possibility of developing a quantitative model for understanding and engineering genetic regulation. Such evidence would help move the idea of reconstructing error control codes for engineered and biological systems from the high risk high payoff realm into the highly probable high payoff domain. Additionally this work will impact biological sensor development and the ability to model and ultimately develop defense mechanisms against bioagents that can be engineered to cause catastrophic damage. Understanding how biological organisms are able to communicate their genetic message efficiently in the presence of noise can improve our current communication protocols, a continuing research interest. Towards this end, project goals include: (1) Develop parameter estimation methods for n for block codes and for n, k, and m for convolutional codes. Use methods to determine error control (EC) code parameters for gene regulatory sequence. (2) Develop an evolutionary computing computational framework for near-optimal solutions to the algebraic code reconstruction problem. Method will be tested on engineered and biological sequences.

  13. Genetic algorithm based optimization of advanced solar cell designs modeled in Silvaco AtlasTM

    OpenAIRE

    Utsler, James

    2006-01-01

    A genetic algorithm was used to optimize the power output of multi-junction solar cells. Solar cell operation was modeled using the Silvaco ATLASTM software. The output of the ATLASTM simulation runs served as the input to the genetic algorithm. The genetic algorithm was run as a diffusing computation on a network of eighteen dual processor nodes. Results showed that the genetic algorithm produced better power output optimizations when compared with the results obtained using the hill cli...

  14. Genetics in eating disorders: extending the boundaries of research

    Directory of Open Access Journals (Sweden)

    Andréa Poyastro Pinheiro

    2006-09-01

    Full Text Available OBJECTIVE: To review the recent literature relevant to genetic research in eating disorders and to discuss unique issues which are crucial for the development of a genetic research project in eating disorders in Brazil. METHOD: A computer literature review was conducted in the Medline database between 1984 and may 2005 with the search terms "eating disorders", "anorexia nervosa", "bulimia nervosa", "binge eating disorder", "family", "twin" and "molecular genetic" studies. RESULTS: Current research findings suggest a substantial influence of genetic factors on the liability to anorexia nervosa and bulimia nervosa. Genetic research with admixed populations should take into consideration sample size, density of genotyping and population stratification. Through admixture mapping it is possible to study the genetic structure of admixed human populations to localize genes that underlie ethnic variation in diseases or traits of interest. CONCLUSIONS: The development of a major collaborative genetics initiative of eating disorders in Brazil and South America would represent a realistic possibility of studying the genetics of eating disorders in the context of inter ethnic groups, and also integrate a new perspective on the biological etiology of eating disorders.

  15. Intelligent computing systems emerging application areas

    CERN Document Server

    Virvou, Maria; Jain, Lakhmi

    2016-01-01

    This book at hand explores emerging scientific and technological areas in which Intelligent Computing Systems provide efficient solutions and, thus, may play a role in the years to come. It demonstrates how Intelligent Computing Systems make use of computational methodologies that mimic nature-inspired processes to address real world problems of high complexity for which exact mathematical solutions, based on physical and statistical modelling, are intractable. Common intelligent computational methodologies are presented including artificial neural networks, evolutionary computation, genetic algorithms, artificial immune systems, fuzzy logic, swarm intelligence, artificial life, virtual worlds and hybrid methodologies based on combinations of the previous. The book will be useful to researchers, practitioners and graduate students dealing with mathematically-intractable problems. It is intended for both the expert/researcher in the field of Intelligent Computing Systems, as well as for the general reader in t...

  16. SOME PARADIGMS OF ARTIFICIAL INTELLIGENCE IN FINANCIAL COMPUTER SYSTEMS

    OpenAIRE

    Jerzy Balicki

    2015-01-01

    The article discusses some paradigms of artificial intelligence in the context of their applications in computer financial systems. The proposed approach has a significant po-tential to increase the competitiveness of enterprises, including financial institutions. However, it requires the effective use of supercomputers, grids and cloud computing. A reference is made to the computing environment for Bitcoin. In addition, we characterized genetic programming and artificial neural networks to p...

  17. Computing patient data in the cloud: practical and legal considerations for genetics and genomics research in Europe and internationally.

    Science.gov (United States)

    Molnár-Gábor, Fruzsina; Lueck, Rupert; Yakneen, Sergei; Korbel, Jan O

    2017-06-20

    Biomedical research is becoming increasingly large-scale and international. Cloud computing enables the comprehensive integration of genomic and clinical data, and the global sharing and collaborative processing of these data within a flexibly scalable infrastructure. Clouds offer novel research opportunities in genomics, as they facilitate cohort studies to be carried out at unprecedented scale, and they enable computer processing with superior pace and throughput, allowing researchers to address questions that could not be addressed by studies using limited cohorts. A well-developed example of such research is the Pan-Cancer Analysis of Whole Genomes project, which involves the analysis of petabyte-scale genomic datasets from research centers in different locations or countries and different jurisdictions. Aside from the tremendous opportunities, there are also concerns regarding the utilization of clouds; these concerns pertain to perceived limitations in data security and protection, and the need for due consideration of the rights of patient donors and research participants. Furthermore, the increased outsourcing of information technology impedes the ability of researchers to act within the realm of existing local regulations owing to fundamental differences in the understanding of the right to data protection in various legal systems. In this Opinion article, we address the current opportunities and limitations of cloud computing and highlight the responsible use of federated and hybrid clouds that are set up between public and private partners as an adequate solution for genetics and genomics research in Europe, and under certain conditions between Europe and international partners. This approach could represent a sensible middle ground between fragmented individual solutions and a "one-size-fits-all" approach.

  18. Parallel Genetic Algorithms for calibrating Cellular Automata models: Application to lava flows

    International Nuclear Information System (INIS)

    D'Ambrosio, D.; Spataro, W.; Di Gregorio, S.; Calabria Univ., Cosenza; Crisci, G.M.; Rongo, R.; Calabria Univ., Cosenza

    2005-01-01

    Cellular Automata are highly nonlinear dynamical systems which are suitable far simulating natural phenomena whose behaviour may be specified in terms of local interactions. The Cellular Automata model SCIARA, developed far the simulation of lava flows, demonstrated to be able to reproduce the behaviour of Etnean events. However, in order to apply the model far the prediction of future scenarios, a thorough calibrating phase is required. This work presents the application of Genetic Algorithms, general-purpose search algorithms inspired to natural selection and genetics, far the parameters optimisation of the model SCIARA. Difficulties due to the elevated computational time suggested the adoption a Master-Slave Parallel Genetic Algorithm far the calibration of the model with respect to the 2001 Mt. Etna eruption. Results demonstrated the usefulness of the approach, both in terms of computing time and quality of performed simulations

  19. A Multi-scale Computational Platform to Mechanistically Assess the Effect of Genetic Variation on Drug Responses in Human Erythrocyte Metabolism.

    Science.gov (United States)

    Mih, Nathan; Brunk, Elizabeth; Bordbar, Aarash; Palsson, Bernhard O

    2016-07-01

    Progress in systems medicine brings promise to addressing patient heterogeneity and individualized therapies. Recently, genome-scale models of metabolism have been shown to provide insight into the mechanistic link between drug therapies and systems-level off-target effects while being expanded to explicitly include the three-dimensional structure of proteins. The integration of these molecular-level details, such as the physical, structural, and dynamical properties of proteins, notably expands the computational description of biochemical network-level properties and the possibility of understanding and predicting whole cell phenotypes. In this study, we present a multi-scale modeling framework that describes biological processes which range in scale from atomistic details to an entire metabolic network. Using this approach, we can understand how genetic variation, which impacts the structure and reactivity of a protein, influences both native and drug-induced metabolic states. As a proof-of-concept, we study three enzymes (catechol-O-methyltransferase, glucose-6-phosphate dehydrogenase, and glyceraldehyde-3-phosphate dehydrogenase) and their respective genetic variants which have clinically relevant associations. Using all-atom molecular dynamic simulations enables the sampling of long timescale conformational dynamics of the proteins (and their mutant variants) in complex with their respective native metabolites or drug molecules. We find that changes in a protein's structure due to a mutation influences protein binding affinity to metabolites and/or drug molecules, and inflicts large-scale changes in metabolism.

  20. RNA secondary structure prediction using soft computing.

    Science.gov (United States)

    Ray, Shubhra Sankar; Pal, Sankar K

    2013-01-01

    Prediction of RNA structure is invaluable in creating new drugs and understanding genetic diseases. Several deterministic algorithms and soft computing-based techniques have been developed for more than a decade to determine the structure from a known RNA sequence. Soft computing gained importance with the need to get approximate solutions for RNA sequences by considering the issues related with kinetic effects, cotranscriptional folding, and estimation of certain energy parameters. A brief description of some of the soft computing-based techniques, developed for RNA secondary structure prediction, is presented along with their relevance. The basic concepts of RNA and its different structural elements like helix, bulge, hairpin loop, internal loop, and multiloop are described. These are followed by different methodologies, employing genetic algorithms, artificial neural networks, and fuzzy logic. The role of various metaheuristics, like simulated annealing, particle swarm optimization, ant colony optimization, and tabu search is also discussed. A relative comparison among different techniques, in predicting 12 known RNA secondary structures, is presented, as an example. Future challenging issues are then mentioned.

  1. Application of genetic algorithms for parameter estimation in liquid chromatography

    International Nuclear Information System (INIS)

    Hernandez Torres, Reynier; Irizar Mesa, Mirtha; Tavares Camara, Leoncio Diogenes

    2012-01-01

    In chromatography, complex inverse problems related to the parameters estimation and process optimization are presented. Metaheuristics methods are known as general purpose approximated algorithms which seek and hopefully find good solutions at a reasonable computational cost. These methods are iterative process to perform a robust search of a solution space. Genetic algorithms are optimization techniques based on the principles of genetics and natural selection. They have demonstrated very good performance as global optimizers in many types of applications, including inverse problems. In this work, the effectiveness of genetic algorithms is investigated to estimate parameters in liquid chromatography

  2. Human Inspired Self-developmental Model of Neural Network (HIM): Introducing Content/Form Computing

    Science.gov (United States)

    Krajíček, Jiří

    This paper presents cross-disciplinary research between medical/psychological evidence on human abilities and informatics needs to update current models in computer science to support alternative methods for computation and communication. In [10] we have already proposed hypothesis introducing concept of human information model (HIM) as cooperative system. Here we continue on HIM design in detail. In our design, first we introduce Content/Form computing system which is new principle of present methods in evolutionary computing (genetic algorithms, genetic programming). Then we apply this system on HIM (type of artificial neural network) model as basic network self-developmental paradigm. Main inspiration of our natural/human design comes from well known concept of artificial neural networks, medical/psychological evidence and Sheldrake theory of "Nature as Alive" [22].

  3. Neural networks for genetic epidemiology: past, present, and future

    Directory of Open Access Journals (Sweden)

    Motsinger-Reif Alison A

    2008-07-01

    Full Text Available Abstract During the past two decades, the field of human genetics has experienced an information explosion. The completion of the human genome project and the development of high throughput SNP technologies have created a wealth of data; however, the analysis and interpretation of these data have created a research bottleneck. While technology facilitates the measurement of hundreds or thousands of genes, statistical and computational methodologies are lacking for the analysis of these data. New statistical methods and variable selection strategies must be explored for identifying disease susceptibility genes for common, complex diseases. Neural networks (NN are a class of pattern recognition methods that have been successfully implemented for data mining and prediction in a variety of fields. The application of NN for statistical genetics studies is an active area of research. Neural networks have been applied in both linkage and association analysis for the identification of disease susceptibility genes. In the current review, we consider how NN have been used for both linkage and association analyses in genetic epidemiology. We discuss both the successes of these initial NN applications, and the questions that arose during the previous studies. Finally, we introduce evolutionary computing strategies, Genetic Programming Neural Networks (GPNN and Grammatical Evolution Neural Networks (GENN, for using NN in association studies of complex human diseases that address some of the caveats illuminated by previous work.

  4. Cordova: web-based management of genetic variation data.

    Science.gov (United States)

    Ephraim, Sean S; Anand, Nikhil; DeLuca, Adam P; Taylor, Kyle R; Kolbe, Diana L; Simpson, Allen C; Azaiez, Hela; Sloan, Christina M; Shearer, A Eliot; Hallier, Andrea R; Casavant, Thomas L; Scheetz, Todd E; Smith, Richard J H; Braun, Terry A

    2014-12-01

    Cordova is an out-of-the-box solution for building and maintaining an online database of genetic variations integrated with pathogenicity prediction results from popular algorithms. Our primary motivation for developing this system is to aid researchers and clinician-scientists in determining the clinical significance of genetic variations. To achieve this goal, Cordova provides an interface to review and manually or computationally curate genetic variation data as well as share it for clinical diagnostics and the advancement of research. Cordova is open source under the MIT license and is freely available for download at https://github.com/clcg/cordova. Published by Oxford University Press. This work is written by US Government employees and is in the public domain in the US.

  5. On the Road to Genetic Boolean Matrix Factorization

    Czech Academy of Sciences Publication Activity Database

    Snášel, V.; Platoš, J.; Krömer, P.; Húsek, Dušan; Frolov, A.

    2007-01-01

    Roč. 17, č. 6 (2007), s. 675-688 ISSN 1210-0552 Institutional research plan: CEZ:AV0Z10300504 Keywords : data mining * genetic algorithms * Boolean factorization * binary data * machine learning * feature extraction Subject RIV: IN - Informatics, Computer Science Impact factor: 0.280, year: 2007

  6. Quantitative genetic activity graphical profiles for use in chemical evaluation

    Energy Technology Data Exchange (ETDEWEB)

    Waters, M.D. [Environmental Protection Agency, Washington, DC (United States); Stack, H.F.; Garrett, N.E.; Jackson, M.A. [Environmental Health Research and Testing, Inc., Research Triangle Park, NC (United States)

    1990-12-31

    A graphic approach, terms a Genetic Activity Profile (GAP), was developed to display a matrix of data on the genetic and related effects of selected chemical agents. The profiles provide a visual overview of the quantitative (doses) and qualitative (test results) data for each chemical. Either the lowest effective dose or highest ineffective dose is recorded for each agent and bioassay. Up to 200 different test systems are represented across the GAP. Bioassay systems are organized according to the phylogeny of the test organisms and the end points of genetic activity. The methodology for producing and evaluating genetic activity profile was developed in collaboration with the International Agency for Research on Cancer (IARC). Data on individual chemicals were compiles by IARC and by the US Environmental Protection Agency (EPA). Data are available on 343 compounds selected from volumes 1-53 of the IARC Monographs and on 115 compounds identified as Superfund Priority Substances. Software to display the GAPs on an IBM-compatible personal computer is available from the authors. Structurally similar compounds frequently display qualitatively and quantitatively similar profiles of genetic activity. Through examination of the patterns of GAPs of pairs and groups of chemicals, it is possible to make more informed decisions regarding the selection of test batteries to be used in evaluation of chemical analogs. GAPs provided useful data for development of weight-of-evidence hazard ranking schemes. Also, some knowledge of the potential genetic activity of complex environmental mixtures may be gained from an assessment of the genetic activity profiles of component chemicals. The fundamental techniques and computer programs devised for the GAP database may be used to develop similar databases in other disciplines. 36 refs., 2 figs.

  7. Disease-Concordant Twins Empower Genetic Association Studies

    DEFF Research Database (Denmark)

    Tan, Qihua; Li, Weilong; Vandin, Fabio

    2017-01-01

    and ordinary healthy samples as controls. We examined the power gain of the twin-based design for various scenarios (i.e., cases from monozygotic and dizygotic twin pairs concordant for a disease) and compared the power with the ordinary case-control design with cases collected from the unrelated patient...... concordant for a disease, should confer increased power in genetic association analysis because of their genetic relatedness. We conducted a computer simulation study to explore the power advantage of the disease-concordant twin design, which uses singletons from disease-concordant twin pairs as cases...... population. Simulation was done by assigning various allele frequencies and allelic relative risks for different mode of genetic inheritance. In general, for achieving a power estimate of 80%, the sample sizes needed for dizygotic and monozygotic twin cases were one half and one fourth of the sample size...

  8. Hunter disease eClinic: interactive, computer-assisted, problem-based approach to independent learning about a rare genetic disease

    Directory of Open Access Journals (Sweden)

    Moldovan Laura

    2010-10-01

    Full Text Available Abstract Background Computer-based teaching (CBT is a well-known educational device, but it has never been applied systematically to the teaching of a complex, rare, genetic disease, such as Hunter disease (MPS II. Aim To develop interactive teaching software functioning as a virtual clinic for the management of MPS II. Implementation and Results The Hunter disease eClinic, a self-training, user-friendly educational software program, available at the Lysosomal Storage Research Group (http://www.lysosomalstorageresearch.ca, was developed using the Adobe Flash multimedia platform. It was designed to function both to provide a realistic, interactive virtual clinic and instantaneous access to supporting literature on Hunter disease. The Hunter disease eClinic consists of an eBook and an eClinic. The eClinic is the interactive virtual clinic component of the software. Within an environment resembling a real clinic, the trainee is instructed to perform a medical history, to examine the patient, and to order appropriate investigation. The program provides clinical data derived from the management of actual patients with Hunter disease. The eBook provides instantaneous, electronic access to a vast collection of reference information to provide detailed background clinical and basic science, including relevant biochemistry, physiology, and genetics. In the eClinic, the trainee is presented with quizzes designed to provide immediate feedback on both trainee effectiveness and efficiency. User feedback on the merits of the program was collected at several seminars and formal clinical rounds at several medical centres, primarily in Canada. In addition, online usage statistics were documented for a 2-year period. Feedback was consistently positive and confirmed the practical benefit of the program. The online English-language version is accessed daily by users from all over the world; a Japanese translation of the program is also available. Conclusions The

  9. Hunter disease eClinic: interactive, computer-assisted, problem-based approach to independent learning about a rare genetic disease.

    Science.gov (United States)

    Al-Jasmi, Fatma; Moldovan, Laura; Clarke, Joe T R

    2010-10-25

    Computer-based teaching (CBT) is a well-known educational device, but it has never been applied systematically to the teaching of a complex, rare, genetic disease, such as Hunter disease (MPS II). To develop interactive teaching software functioning as a virtual clinic for the management of MPS II. The Hunter disease eClinic, a self-training, user-friendly educational software program, available at the Lysosomal Storage Research Group (http://www.lysosomalstorageresearch.ca), was developed using the Adobe Flash multimedia platform. It was designed to function both to provide a realistic, interactive virtual clinic and instantaneous access to supporting literature on Hunter disease. The Hunter disease eClinic consists of an eBook and an eClinic. The eClinic is the interactive virtual clinic component of the software. Within an environment resembling a real clinic, the trainee is instructed to perform a medical history, to examine the patient, and to order appropriate investigation. The program provides clinical data derived from the management of actual patients with Hunter disease. The eBook provides instantaneous, electronic access to a vast collection of reference information to provide detailed background clinical and basic science, including relevant biochemistry, physiology, and genetics. In the eClinic, the trainee is presented with quizzes designed to provide immediate feedback on both trainee effectiveness and efficiency. User feedback on the merits of the program was collected at several seminars and formal clinical rounds at several medical centres, primarily in Canada. In addition, online usage statistics were documented for a 2-year period. Feedback was consistently positive and confirmed the practical benefit of the program. The online English-language version is accessed daily by users from all over the world; a Japanese translation of the program is also available. The Hunter disease eClinic employs a CBT model providing the trainee with realistic

  10. A controlled genetic algorithm by fuzzy logic and belief functions for job-shop scheduling.

    Science.gov (United States)

    Hajri, S; Liouane, N; Hammadi, S; Borne, P

    2000-01-01

    Most scheduling problems are highly complex combinatorial problems. However, stochastic methods such as genetic algorithm yield good solutions. In this paper, we present a controlled genetic algorithm (CGA) based on fuzzy logic and belief functions to solve job-shop scheduling problems. For better performance, we propose an efficient representational scheme, heuristic rules for creating the initial population, and a new methodology for mixing and computing genetic operator probabilities.

  11. A Hybrid Genetic Algorithm Approach for Optimal Power Flow

    Directory of Open Access Journals (Sweden)

    Sydulu Maheswarapu

    2011-08-01

    Full Text Available This paper puts forward a reformed hybrid genetic algorithm (GA based approach to the optimal power flow. In the approach followed here, continuous variables are designed using real-coded GA and discrete variables are processed as binary strings. The outcomes are compared with many other methods like simple genetic algorithm (GA, adaptive genetic algorithm (AGA, differential evolution (DE, particle swarm optimization (PSO and music based harmony search (MBHS on a IEEE30 bus test bed, with a total load of 283.4 MW. Its found that the proposed algorithm is found to offer lowest fuel cost. The proposed method is found to be computationally faster, robust, superior and promising form its convergence characteristics.

  12. Proceedings of the 2011 International Conference on Informatics, Cybernetics, and Computer Engineering

    CERN Document Server

    2012-01-01

    The volume includes a set of selected papers extended and revised from the International Conference on Informatics, Cybernetics, and Computer Engineering. Intelligent control is a class of control techniques, that use various AI computing approaches like neural networks, Bayesian probability, fuzzy logic, machine learning, evolutionary computation and genetic algorithms. Intelligent control can be divided into the following major sub-domains: Neural network control Bayesian control Fuzzy (logic) control Neuro-fuzzy control Expert Systems Genetic control Intelligent agents (Cognitive/Conscious control) New control techniques are created continuously as new models of intelligent behavior are created and computational methods developed to support them. Networks may be classified according to a wide variety of characteristics such as medium used to transport the data, communications protocol used, scale, topology, organizational scope, etc. ICCE 2011 Volume 1 is to provide a forum for researchers, educators, engi...

  13. SoftLab: A Soft-Computing Software for Experimental Research with Commercialization Aspects

    Science.gov (United States)

    Akbarzadeh-T, M.-R.; Shaikh, T. S.; Ren, J.; Hubbell, Rob; Kumbla, K. K.; Jamshidi, M

    1998-01-01

    SoftLab is a software environment for research and development in intelligent modeling/control using soft-computing paradigms such as fuzzy logic, neural networks, genetic algorithms, and genetic programs. SoftLab addresses the inadequacies of the existing soft-computing software by supporting comprehensive multidisciplinary functionalities from management tools to engineering systems. Furthermore, the built-in features help the user process/analyze information more efficiently by a friendly yet powerful interface, and will allow the user to specify user-specific processing modules, hence adding to the standard configuration of the software environment.

  14. Training Software in Artificial-Intelligence Computing Techniques

    Science.gov (United States)

    Howard, Ayanna; Rogstad, Eric; Chalfant, Eugene

    2005-01-01

    The Artificial Intelligence (AI) Toolkit is a computer program for training scientists, engineers, and university students in three soft-computing techniques (fuzzy logic, neural networks, and genetic algorithms) used in artificial-intelligence applications. The program promotes an easily understandable tutorial interface, including an interactive graphical component through which the user can gain hands-on experience in soft-computing techniques applied to realistic example problems. The tutorial provides step-by-step instructions on the workings of soft-computing technology, whereas the hands-on examples allow interaction and reinforcement of the techniques explained throughout the tutorial. In the fuzzy-logic example, a user can interact with a robot and an obstacle course to verify how fuzzy logic is used to command a rover traverse from an arbitrary start to the goal location. For the genetic algorithm example, the problem is to determine the minimum-length path for visiting a user-chosen set of planets in the solar system. For the neural-network example, the problem is to decide, on the basis of input data on physical characteristics, whether a person is a man, woman, or child. The AI Toolkit is compatible with the Windows 95,98, ME, NT 4.0, 2000, and XP operating systems. A computer having a processor speed of at least 300 MHz, and random-access memory of at least 56MB is recommended for optimal performance. The program can be run on a slower computer having less memory, but some functions may not be executed properly.

  15. Population genetics without intraspecific data

    DEFF Research Database (Denmark)

    Thorne, Jeffrey L; Choi, Sang Chul; Yu, Jiaye

    2007-01-01

    A central goal of computational biology is the prediction of phenotype from DNA and protein sequence data. Recent models of sequence change use in silico prediction systems to incorporate the effects of phenotype on evolutionary rates. These models have been designed for analyzing sequence data...... populations, and parameters of interspecific models should have population genetic interpretations. We show, with two examples, how population genetic interpretations can be assigned to evolutionary models. The first example considers the impact of RNA secondary structure on sequence change, and the second...... reflects the tendency for protein tertiary structure to influence nonsynonymous substitution rates. We argue that statistical fit to data should not be the sole criterion for assessing models of sequence change. A good interspecific model should also yield a clear and biologically plausible population...

  16. How Complex, Probable, and Predictable is Genetically Driven Red Queen Chaos?

    Science.gov (United States)

    Duarte, Jorge; Rodrigues, Carla; Januário, Cristina; Martins, Nuno; Sardanyés, Josep

    2015-12-01

    Coevolution between two antagonistic species has been widely studied theoretically for both ecologically- and genetically-driven Red Queen dynamics. A typical outcome of these systems is an oscillatory behavior causing an endless series of one species adaptation and others counter-adaptation. More recently, a mathematical model combining a three-species food chain system with an adaptive dynamics approach revealed genetically driven chaotic Red Queen coevolution. In the present article, we analyze this mathematical model mainly focusing on the impact of species rates of evolution (mutation rates) in the dynamics. Firstly, we analytically proof the boundedness of the trajectories of the chaotic attractor. The complexity of the coupling between the dynamical variables is quantified using observability indices. By using symbolic dynamics theory, we quantify the complexity of genetically driven Red Queen chaos computing the topological entropy of existing one-dimensional iterated maps using Markov partitions. Co-dimensional two bifurcation diagrams are also built from the period ordering of the orbits of the maps. Then, we study the predictability of the Red Queen chaos, found in narrow regions of mutation rates. To extend the previous analyses, we also computed the likeliness of finding chaos in a given region of the parameter space varying other model parameters simultaneously. Such analyses allowed us to compute a mean predictability measure for the system in the explored region of the parameter space. We found that genetically driven Red Queen chaos, although being restricted to small regions of the analyzed parameter space, might be highly unpredictable.

  17. SOME PARADIGMS OF ARTIFICIAL INTELLIGENCE IN FINANCIAL COMPUTER SYSTEMS

    Directory of Open Access Journals (Sweden)

    Jerzy Balicki

    2015-12-01

    Full Text Available The article discusses some paradigms of artificial intelligence in the context of their applications in computer financial systems. The proposed approach has a significant po-tential to increase the competitiveness of enterprises, including financial institutions. However, it requires the effective use of supercomputers, grids and cloud computing. A reference is made to the computing environment for Bitcoin. In addition, we characterized genetic programming and artificial neural networks to prepare investment strategies on the stock exchange market.

  18. Computational and Organotypic Modeling of Microcephaly (Teratology Society)

    Science.gov (United States)

    Microcephaly is associated with reduced cortical surface area and ventricular dilations. Many genetic and environmental factors precipitate this malformation, including prenatal alcohol exposure and maternal Zika infection. This complexity motivates the engineering of computation...

  19. Information transmission in genetic regulatory networks: a review

    International Nuclear Information System (INIS)

    Tkacik, Gasper; Walczak, Aleksandra M

    2011-01-01

    Genetic regulatory networks enable cells to respond to changes in internal and external conditions by dynamically coordinating their gene expression profiles. Our ability to make quantitative measurements in these biochemical circuits has deepened our understanding of what kinds of computations genetic regulatory networks can perform, and with what reliability. These advances have motivated researchers to look for connections between the architecture and function of genetic regulatory networks. Transmitting information between a network's inputs and outputs has been proposed as one such possible measure of function, relevant in certain biological contexts. Here we summarize recent developments in the application of information theory to gene regulatory networks. We first review basic concepts in information theory necessary for understanding recent work. We then discuss the functional complexity of gene regulation, which arises from the molecular nature of the regulatory interactions. We end by reviewing some experiments that support the view that genetic networks responsible for early development of multicellular organisms might be maximizing transmitted 'positional information'. (topical review)

  20. Genetic Synthesis of New Reversible/Quantum Ternary Comparator

    Directory of Open Access Journals (Sweden)

    DEIBUK, V.

    2015-08-01

    Full Text Available Methods of quantum/reversible logic synthesis are based on the use of the binary nature of quantum computing. However, multiple-valued logic is a promising choice for future quantum computer technology due to a number of advantages over binary circuits. In this paper we have developed a synthesis of ternary reversible circuits based on Muthukrishnan-Stroud gates using a genetic algorithm. The method of coding chromosome is presented, and well-grounded choice of algorithm parameters allowed obtaining better circuit schemes of one- and n-qutrit ternary comparators compared with other methods. These parameters are quantum cost of received reversible devices, delay time and number of constant input (ancilla lines. Proposed implementation of the genetic algorithm has led to reducing of the device delay time and the number of ancilla qutrits to 1 and 2n-1 for one- and n-qutrits full comparators, respectively. For designing of n-qutrit comparator we have introduced a complementary device which compares output functions of 1-qutrit comparators.

  1. Part E: Evolutionary Computation

    DEFF Research Database (Denmark)

    2015-01-01

    of Computational Intelligence. First, comprehensive surveys of genetic algorithms, genetic programming, evolution strategies, parallel evolutionary algorithms are presented, which are readable and constructive so that a large audience might find them useful and – to some extent – ready to use. Some more general...... kinds of evolutionary algorithms, have been prudently analyzed. This analysis was followed by a thorough analysis of various issues involved in stochastic local search algorithms. An interesting survey of various technological and industrial applications in mechanical engineering and design has been...... topics like the estimation of distribution algorithms, indicator-based selection, etc., are also discussed. An important problem, from a theoretical and practical point of view, of learning classifier systems is presented in depth. Multiobjective evolutionary algorithms, which constitute one of the most...

  2. Using a Computer Animation to Teach High School Molecular Biology

    Science.gov (United States)

    Rotbain, Yosi; Marbach-Ad, Gili; Stavy, Ruth

    2008-01-01

    We present an active way to use a computer animation in secondary molecular genetics class. For this purpose we developed an activity booklet that helps students to work interactively with a computer animation which deals with abstract concepts and processes in molecular biology. The achievements of the experimental group were compared with those…

  3. ABCtoolbox: a versatile toolkit for approximate Bayesian computations

    Directory of Open Access Journals (Sweden)

    Neuenschwander Samuel

    2010-03-01

    Full Text Available Abstract Background The estimation of demographic parameters from genetic data often requires the computation of likelihoods. However, the likelihood function is computationally intractable for many realistic evolutionary models, and the use of Bayesian inference has therefore been limited to very simple models. The situation changed recently with the advent of Approximate Bayesian Computation (ABC algorithms allowing one to obtain parameter posterior distributions based on simulations not requiring likelihood computations. Results Here we present ABCtoolbox, a series of open source programs to perform Approximate Bayesian Computations (ABC. It implements various ABC algorithms including rejection sampling, MCMC without likelihood, a Particle-based sampler and ABC-GLM. ABCtoolbox is bundled with, but not limited to, a program that allows parameter inference in a population genetics context and the simultaneous use of different types of markers with different ploidy levels. In addition, ABCtoolbox can also interact with most simulation and summary statistics computation programs. The usability of the ABCtoolbox is demonstrated by inferring the evolutionary history of two evolutionary lineages of Microtus arvalis. Using nuclear microsatellites and mitochondrial sequence data in the same estimation procedure enabled us to infer sex-specific population sizes and migration rates and to find that males show smaller population sizes but much higher levels of migration than females. Conclusion ABCtoolbox allows a user to perform all the necessary steps of a full ABC analysis, from parameter sampling from prior distributions, data simulations, computation of summary statistics, estimation of posterior distributions, model choice, validation of the estimation procedure, and visualization of the results.

  4. Comparison of evolutionary computation algorithms for solving bi ...

    Indian Academy of Sciences (India)

    failure probability. Multiobjective Evolutionary Computation algorithms (MOEAs) are well-suited for Multiobjective task scheduling on heterogeneous environment. The two Multi-Objective Evolutionary Algorithms such as Multiobjective Genetic. Algorithm (MOGA) and Multiobjective Evolutionary Programming (MOEP) with.

  5. A Multi-scale Computational Platform to Mechanistically Assess the Effect of Genetic Variation on Drug Responses in Human Erythrocyte Metabolism.

    Directory of Open Access Journals (Sweden)

    Nathan Mih

    2016-07-01

    Full Text Available Progress in systems medicine brings promise to addressing patient heterogeneity and individualized therapies. Recently, genome-scale models of metabolism have been shown to provide insight into the mechanistic link between drug therapies and systems-level off-target effects while being expanded to explicitly include the three-dimensional structure of proteins. The integration of these molecular-level details, such as the physical, structural, and dynamical properties of proteins, notably expands the computational description of biochemical network-level properties and the possibility of understanding and predicting whole cell phenotypes. In this study, we present a multi-scale modeling framework that describes biological processes which range in scale from atomistic details to an entire metabolic network. Using this approach, we can understand how genetic variation, which impacts the structure and reactivity of a protein, influences both native and drug-induced metabolic states. As a proof-of-concept, we study three enzymes (catechol-O-methyltransferase, glucose-6-phosphate dehydrogenase, and glyceraldehyde-3-phosphate dehydrogenase and their respective genetic variants which have clinically relevant associations. Using all-atom molecular dynamic simulations enables the sampling of long timescale conformational dynamics of the proteins (and their mutant variants in complex with their respective native metabolites or drug molecules. We find that changes in a protein's structure due to a mutation influences protein binding affinity to metabolites and/or drug molecules, and inflicts large-scale changes in metabolism.

  6. Genetic Algorithms for the Optimization of Chemical Processes Based on Problem Descriptions

    Czech Academy of Sciences Publication Activity Database

    Holeňa, Martin; Rodemerck, U.; Čukić, T.; Linke, D.; Dingerdissen, U.

    2007-01-01

    Roč. 6, č. 4 (2007), s. 615-621 ISSN 1109-2769 R&D Projects: GA ČR GA201/05/0325 Institutional research plan: CEZ:AV0Z10300504 Keywords : computer applications in chemistry * optimization methods * empirical objective function * genetic algorithms * problem-tailoring * formal description language * program generator Subject RIV: IN - Informatics, Computer Science

  7. A Constrained Genetic Algorithm with Adaptively Defined Fitness Function in MRS Quantification

    Science.gov (United States)

    Papakostas, G. A.; Karras, D. A.; Mertzios, B. G.; Graveron-Demilly, D.; van Ormondt, D.

    MRS Signal quantification is a rather involved procedure and has attracted the interest of the medical engineering community, regarding the development of computationally efficient methodologies. Significant contributions based on Computational Intelligence tools, such as Neural Networks (NNs), demonstrated a good performance but not without drawbacks already discussed by the authors. On the other hand preliminary application of Genetic Algorithms (GA) has already been reported in the literature by the authors regarding the peak detection problem encountered in MRS quantification using the Voigt line shape model. This paper investigates a novel constrained genetic algorithm involving a generic and adaptively defined fitness function which extends the simple genetic algorithm methodology in case of noisy signals. The applicability of this new algorithm is scrutinized through experimentation in artificial MRS signals interleaved with noise, regarding its signal fitting capabilities. Although extensive experiments with real world MRS signals are necessary, the herein shown performance illustrates the method's potential to be established as a generic MRS metabolites quantification procedure.

  8. Evolutionary computation in zoology and ecology.

    Science.gov (United States)

    Boone, Randall B

    2017-12-01

    Evolutionary computational methods have adopted attributes of natural selection and evolution to solve problems in computer science, engineering, and other fields. The method is growing in use in zoology and ecology. Evolutionary principles may be merged with an agent-based modeling perspective to have individual animals or other agents compete. Four main categories are discussed: genetic algorithms, evolutionary programming, genetic programming, and evolutionary strategies. In evolutionary computation, a population is represented in a way that allows for an objective function to be assessed that is relevant to the problem of interest. The poorest performing members are removed from the population, and remaining members reproduce and may be mutated. The fitness of the members is again assessed, and the cycle continues until a stopping condition is met. Case studies include optimizing: egg shape given different clutch sizes, mate selection, migration of wildebeest, birds, and elk, vulture foraging behavior, algal bloom prediction, and species richness given energy constraints. Other case studies simulate the evolution of species and a means to project shifts in species ranges in response to a changing climate that includes competition and phenotypic plasticity. This introduction concludes by citing other uses of evolutionary computation and a review of the flexibility of the methods. For example, representing species' niche spaces subject to selective pressure allows studies on cladistics, the taxon cycle, neutral versus niche paradigms, fundamental versus realized niches, community structure and order of colonization, invasiveness, and responses to a changing climate.

  9. Computer program for allocation of generators in isolated systems of direct current using genetic algorithm; Programa computacional para alocacao de geradores em sistemas isolados de corrente continua utilizando algoritmo genetico

    Energy Technology Data Exchange (ETDEWEB)

    Gewehr, Diego N.; Vargas, Ricardo B.; Melo, Eduardo D. de; Paschoareli Junior, Dionizio [Universidade Estadual Paulista (DEE/UNESP), Ilha Solteira, SP (Brazil). Dept. de Engenharia Eletrica. Grupo de Pesquisa em Fontes Alternativas e Aproveitamento de Energia

    2008-07-01

    This paper presents a methodology for electric power sources location in isolated direct current micro grids, using genetic algorithm. In this work, photovoltaic panels are considered, although the methodology can be extended for any kind of DC sources. A computational tool is developed using the Matlab simulator, to obtain the best dc system configuration for reduction of panels quantity and costs, and to improve the system performance. (author)

  10. Genetics and Early Detection in Idiopathic Pulmonary Fibrosis

    Science.gov (United States)

    Putman, Rachel K.; Rosas, Ivan O.

    2014-01-01

    Genetic studies hold promise in helping to identify patients with early idiopathic pulmonary fibrosis (IPF). Recent studies using chest computed tomograms (CTs) in smokers and in the general population have demonstrated that imaging abnormalities suggestive of an early stage of pulmonary fibrosis are not uncommon and are associated with respiratory symptoms, physical examination abnormalities, and physiologic decrements expected, but less severe than those noted in patients with IPF. Similarly, recent genetic studies have demonstrated strong and replicable associations between a common promoter polymorphism in the mucin 5B gene (MUC5B) and both IPF and the presence of abnormal imaging findings in the general population. Despite these findings, it is important to note that the definition of early-stage IPF remains unclear, limited data exist to definitively connect abnormal imaging findings to IPF, and genetic studies assessing early-stage pulmonary fibrosis remain in their infancy. In this perspective we provide updated information on interstitial lung abnormalities and their connection to IPF. We summarize information on the genetics of pulmonary fibrosis by focusing on the recent genetic findings of MUC5B. Finally, we discuss the implications of these findings and suggest a roadmap for the use of genetics in the detection of early IPF. PMID:24547893

  11. Genetic Algorithms for Case Adaptation

    Energy Technology Data Exchange (ETDEWEB)

    Salem, A M [Computer Science Dept, Faculty of Computer and Information Sciences, Ain Shams University, Cairo (Egypt); Mohamed, A H [Solid State Dept., (NCRRT), Cairo (Egypt)

    2008-07-01

    Case based reasoning (CBR) paradigm has been widely used to provide computer support for recalling and adapting known cases to novel situations. Case adaptation algorithms generally rely on knowledge based and heuristics in order to change the past solutions to solve new problems. However, case adaptation has always been a difficult process to engineers within (CBR) cycle. Its difficulties can be referred to its domain dependency; and computational cost. In an effort to solve this problem, this research explores a general-purpose method that applying a genetic algorithm (GA) to CBR adaptation. Therefore, it can decrease the computational complexity of the search space in the problems having a great dependency on their domain knowledge. The proposed model can be used to perform a variety of design tasks on a broad set of application domains. However, it has been implemented for the tablet formulation as a domain of application. The proposed system has improved the performance of the CBR design systems.

  12. Genetic Algorithms for Case Adaptation

    International Nuclear Information System (INIS)

    Salem, A.M.; Mohamed, A.H.

    2008-01-01

    Case based reasoning (CBR) paradigm has been widely used to provide computer support for recalling and adapting known cases to novel situations. Case adaptation algorithms generally rely on knowledge based and heuristics in order to change the past solutions to solve new problems. However, case adaptation has always been a difficult process to engineers within (CBR) cycle. Its difficulties can be referred to its domain dependency; and computational cost. In an effort to solve this problem, this research explores a general-purpose method that applying a genetic algorithm (GA) to CBR adaptation. Therefore, it can decrease the computational complexity of the search space in the problems having a great dependency on their domain knowledge. The proposed model can be used to perform a variety of design tasks on a broad set of application domains. However, it has been implemented for the tablet formulation as a domain of application. The proposed system has improved the performance of the CBR design systems

  13. Hybrid Modeling KMeans – Genetic Algorithms in the Health Care Data

    Directory of Open Access Journals (Sweden)

    Tessy Badriyah

    2013-06-01

    Full Text Available K-Means is one of the major algorithms widely used in clustering due to its good computational performance. However, K-Means is very sensitive to the initially selected points which randomly selected, and therefore it does not always generate optimum solutions. Genetic algorithm approach can be applied to solve this problem. In this research we examine the potential of applying hybrid GA- KMeans with focus on the area of health care data. We proposed a new technique using hybrid method combining KMeans Clustering and Genetic Algorithms, called the “Hybrid K-Means Genetic Algorithms” (HKGA. HKGA combines the power of Genetic Algorithms and the efficiency of K-Means Clustering. We compare our results with other conventional algorithms and also with other published research as well. Our results demonstrate that the HKGA achieves very good results and in some cases superior to other methods. Keywords: Machine Learning, K-Means, Genetic Algorithms, Hybrid KMeans Genetic Algorithm (HGKA.

  14. Solving a Hamiltonian Path Problem with a bacterial computer

    Directory of Open Access Journals (Sweden)

    Treece Jessica

    2009-07-01

    Full Text Available Abstract Background The Hamiltonian Path Problem asks whether there is a route in a directed graph from a beginning node to an ending node, visiting each node exactly once. The Hamiltonian Path Problem is NP complete, achieving surprising computational complexity with modest increases in size. This challenge has inspired researchers to broaden the definition of a computer. DNA computers have been developed that solve NP complete problems. Bacterial computers can be programmed by constructing genetic circuits to execute an algorithm that is responsive to the environment and whose result can be observed. Each bacterium can examine a solution to a mathematical problem and billions of them can explore billions of possible solutions. Bacterial computers can be automated, made responsive to selection, and reproduce themselves so that more processing capacity is applied to problems over time. Results We programmed bacteria with a genetic circuit that enables them to evaluate all possible paths in a directed graph in order to find a Hamiltonian path. We encoded a three node directed graph as DNA segments that were autonomously shuffled randomly inside bacteria by a Hin/hixC recombination system we previously adapted from Salmonella typhimurium for use in Escherichia coli. We represented nodes in the graph as linked halves of two different genes encoding red or green fluorescent proteins. Bacterial populations displayed phenotypes that reflected random ordering of edges in the graph. Individual bacterial clones that found a Hamiltonian path reported their success by fluorescing both red and green, resulting in yellow colonies. We used DNA sequencing to verify that the yellow phenotype resulted from genotypes that represented Hamiltonian path solutions, demonstrating that our bacterial computer functioned as expected. Conclusion We successfully designed, constructed, and tested a bacterial computer capable of finding a Hamiltonian path in a three node

  15. Solving a Hamiltonian Path Problem with a bacterial computer

    Science.gov (United States)

    Baumgardner, Jordan; Acker, Karen; Adefuye, Oyinade; Crowley, Samuel Thomas; DeLoache, Will; Dickson, James O; Heard, Lane; Martens, Andrew T; Morton, Nickolaus; Ritter, Michelle; Shoecraft, Amber; Treece, Jessica; Unzicker, Matthew; Valencia, Amanda; Waters, Mike; Campbell, A Malcolm; Heyer, Laurie J; Poet, Jeffrey L; Eckdahl, Todd T

    2009-01-01

    Background The Hamiltonian Path Problem asks whether there is a route in a directed graph from a beginning node to an ending node, visiting each node exactly once. The Hamiltonian Path Problem is NP complete, achieving surprising computational complexity with modest increases in size. This challenge has inspired researchers to broaden the definition of a computer. DNA computers have been developed that solve NP complete problems. Bacterial computers can be programmed by constructing genetic circuits to execute an algorithm that is responsive to the environment and whose result can be observed. Each bacterium can examine a solution to a mathematical problem and billions of them can explore billions of possible solutions. Bacterial computers can be automated, made responsive to selection, and reproduce themselves so that more processing capacity is applied to problems over time. Results We programmed bacteria with a genetic circuit that enables them to evaluate all possible paths in a directed graph in order to find a Hamiltonian path. We encoded a three node directed graph as DNA segments that were autonomously shuffled randomly inside bacteria by a Hin/hixC recombination system we previously adapted from Salmonella typhimurium for use in Escherichia coli. We represented nodes in the graph as linked halves of two different genes encoding red or green fluorescent proteins. Bacterial populations displayed phenotypes that reflected random ordering of edges in the graph. Individual bacterial clones that found a Hamiltonian path reported their success by fluorescing both red and green, resulting in yellow colonies. We used DNA sequencing to verify that the yellow phenotype resulted from genotypes that represented Hamiltonian path solutions, demonstrating that our bacterial computer functioned as expected. Conclusion We successfully designed, constructed, and tested a bacterial computer capable of finding a Hamiltonian path in a three node directed graph. This proof

  16. Synthesizing genetic sequential logic circuit with clock pulse generator.

    Science.gov (United States)

    Chuang, Chia-Hua; Lin, Chun-Liang

    2014-05-28

    Rhythmic clock widely occurs in biological systems which controls several aspects of cell physiology. For the different cell types, it is supplied with various rhythmic frequencies. How to synthesize a specific clock signal is a preliminary but a necessary step to further development of a biological computer in the future. This paper presents a genetic sequential logic circuit with a clock pulse generator based on a synthesized genetic oscillator, which generates a consecutive clock signal whose frequency is an inverse integer multiple to that of the genetic oscillator. An analogous electronic waveform-shaping circuit is constructed by a series of genetic buffers to shape logic high/low levels of an oscillation input in a basic sinusoidal cycle and generate a pulse-width-modulated (PWM) output with various duty cycles. By controlling the threshold level of the genetic buffer, a genetic clock pulse signal with its frequency consistent to the genetic oscillator is synthesized. A synchronous genetic counter circuit based on the topology of the digital sequential logic circuit is triggered by the clock pulse to synthesize the clock signal with an inverse multiple frequency to the genetic oscillator. The function acts like a frequency divider in electronic circuits which plays a key role in the sequential logic circuit with specific operational frequency. A cascaded genetic logic circuit generating clock pulse signals is proposed. Based on analogous implement of digital sequential logic circuits, genetic sequential logic circuits can be constructed by the proposed approach to generate various clock signals from an oscillation signal.

  17. A Primer on High-Throughput Computing for Genomic Selection

    Directory of Open Access Journals (Sweden)

    Xiao-Lin eWu

    2011-02-01

    Full Text Available High-throughput computing (HTC uses computer clusters to solve advanced computational problems, with the goal of accomplishing high throughput over relatively long periods of time. In genomic selection, for example, a set of markers covering the entire genome is used to train a model based on known data, and the resulting model is used to predict the genetic merit of selection candidates. Sophisticated models are very computationally demanding and, with several traits to be evaluated sequentially, computing time is long and output is low. In this paper, we present scenarios and basic principles of how HTC can be used in genomic selection, implemented using various techniques from simple batch processing to pipelining in distributed computer clusters. Various scripting languages, such as shell scripting, Perl and R, are also very useful to devise pipelines. By pipelining, we can reduce total computing time and consequently increase throughput. In comparison to the traditional data processing pipeline residing on the central processors, performing general purpose computation on a graphics processing unit (GPU provide a new-generation approach to massive parallel computing in genomic selection. While the concept of HTC may still be new to many researchers in animal breeding, plant breeding, and genetics, HTC infrastructures have already been built in many institutions, such as the University of Wisconsin – Madison, which can be leveraged for genomic selection, in terms of central processing unit (CPU capacity, network connectivity, storage availability, and middleware connectivity. Exploring existing HTC infrastructures as well as general purpose computing environments will further expand our capability to meet increasing computing demands posed by unprecedented genomic data that we have today. We anticipate that HTC will impact genomic selection via better statistical models, faster solutions, and more competitive products (e.g., from design of

  18. Where genetic algorithms excel.

    Science.gov (United States)

    Baum, E B; Boneh, D; Garrett, C

    2001-01-01

    We analyze the performance of a genetic algorithm (GA) we call Culling, and a variety of other algorithms, on a problem we refer to as the Additive Search Problem (ASP). We show that the problem of learning the Ising perceptron is reducible to a noisy version of ASP. Noisy ASP is the first problem we are aware of where a genetic-type algorithm bests all known competitors. We generalize ASP to k-ASP to study whether GAs will achieve "implicit parallelism" in a problem with many more schemata. GAs fail to achieve this implicit parallelism, but we describe an algorithm we call Explicitly Parallel Search that succeeds. We also compute the optimal culling point for selective breeding, which turns out to be independent of the fitness function or the population distribution. We also analyze a mean field theoretic algorithm performing similarly to Culling on many problems. These results provide insight into when and how GAs can beat competing methods.

  19. A genetic algorithm for solving supply chain network design model

    Science.gov (United States)

    Firoozi, Z.; Ismail, N.; Ariafar, S. H.; Tang, S. H.; Ariffin, M. K. M. A.

    2013-09-01

    Network design is by nature costly and optimization models play significant role in reducing the unnecessary cost components of a distribution network. This study proposes a genetic algorithm to solve a distribution network design model. The structure of the chromosome in the proposed algorithm is defined in a novel way that in addition to producing feasible solutions, it also reduces the computational complexity of the algorithm. Computational results are presented to show the algorithm performance.

  20. Reverse engineering large-scale genetic networks: synthetic versus ...

    Indian Academy of Sciences (India)

    2010-04-19

    Apr 19, 2010 ... process computationally to describe the structure of the sys- tem and ... to the different mathematical formalisms used to model net-. Keywords. gene ..... All the algorithms were implemented in MATLAB 7.0 and run on all the 'gold ..... De Jong H. 2002 Medeling and simulation of genetic regulatory systems: a ...

  1. Genetic modification and genetic determinism

    Science.gov (United States)

    Resnik, David B; Vorhaus, Daniel B

    2006-01-01

    In this article we examine four objections to the genetic modification of human beings: the freedom argument, the giftedness argument, the authenticity argument, and the uniqueness argument. We then demonstrate that each of these arguments against genetic modification assumes a strong version of genetic determinism. Since these strong deterministic assumptions are false, the arguments against genetic modification, which assume and depend upon these assumptions, are therefore unsound. Serious discussion of the morality of genetic modification, and the development of sound science policy, should be driven by arguments that address the actual consequences of genetic modification for individuals and society, not by ones propped up by false or misleading biological assumptions. PMID:16800884

  2. Genetic modification and genetic determinism

    Directory of Open Access Journals (Sweden)

    Vorhaus Daniel B

    2006-06-01

    Full Text Available Abstract In this article we examine four objections to the genetic modification of human beings: the freedom argument, the giftedness argument, the authenticity argument, and the uniqueness argument. We then demonstrate that each of these arguments against genetic modification assumes a strong version of genetic determinism. Since these strong deterministic assumptions are false, the arguments against genetic modification, which assume and depend upon these assumptions, are therefore unsound. Serious discussion of the morality of genetic modification, and the development of sound science policy, should be driven by arguments that address the actual consequences of genetic modification for individuals and society, not by ones propped up by false or misleading biological assumptions.

  3. Electrochemical sensor for multiplex screening of genetically modified DNA: identification of biotech crops by logic-based biomolecular analysis.

    Science.gov (United States)

    Liao, Wei-Ching; Chuang, Min-Chieh; Ho, Ja-An Annie

    2013-12-15

    Genetically modified (GM) technique, one of the modern biomolecular engineering technologies, has been deemed as profitable strategy to fight against global starvation. Yet rapid and reliable analytical method is deficient to evaluate the quality and potential risk of such resulting GM products. We herein present a biomolecular analytical system constructed with distinct biochemical activities to expedite the computational detection of genetically modified organisms (GMOs). The computational mechanism provides an alternative to the complex procedures commonly involved in the screening of GMOs. Given that the bioanalytical system is capable of processing promoter, coding and species genes, affirmative interpretations succeed to identify specified GM event in terms of both electrochemical and optical fashions. The biomolecular computational assay exhibits detection capability of genetically modified DNA below sub-nanomolar level and is found interference-free by abundant coexistence of non-GM DNA. This bioanalytical system, furthermore, sophisticates in array fashion operating multiplex screening against variable GM events. Such a biomolecular computational assay and biosensor holds great promise for rapid, cost-effective, and high-fidelity screening of GMO. Copyright © 2013 Elsevier B.V. All rights reserved.

  4. Accelerating artificial intelligence with reconfigurable computing

    Science.gov (United States)

    Cieszewski, Radoslaw

    Reconfigurable computing is emerging as an important area of research in computer architectures and software systems. Many algorithms can be greatly accelerated by placing the computationally intense portions of an algorithm into reconfigurable hardware. Reconfigurable computing combines many benefits of both software and ASIC implementations. Like software, the mapped circuit is flexible, and can be changed over the lifetime of the system. Similar to an ASIC, reconfigurable systems provide a method to map circuits into hardware. Reconfigurable systems therefore have the potential to achieve far greater performance than software as a result of bypassing the fetch-decode-execute operations of traditional processors, and possibly exploiting a greater level of parallelism. Such a field, where there is many different algorithms which can be accelerated, is an artificial intelligence. This paper presents example hardware implementations of Artificial Neural Networks, Genetic Algorithms and Expert Systems.

  5. Genetics Research Discovered in a Bestseller | Poster

    Science.gov (United States)

    By Nancy Parrish, Staff Writer One morning in early January, Amar Klar sat down at his computer and found an e-mail with a curious message from a colleague. While reading a bestselling novel, The Marriage Plot by Jeffrey Eugenides, his colleague, a professor at Princeton University, found a description of research on yeast genetics that was surprisingly similar to Klar’s early

  6. Liposarcoma or lipoma: Does genetics change classic imaging criteria?

    International Nuclear Information System (INIS)

    Bidault, F.; Vanel, D.; Terrier, Ph.; Jalaguier, A.; Bonvalot, S.; Pedeutour, F.; Couturier, J.M.; Dromain, C.

    2009-01-01

    Differentiating benign from malignant fatty tumours has always been very difficult for both radiologists and pathologists. Cytogenetic and molecular genetic analyses provide complementary tools for differentiating soft tissue tumours. Our objective was to compare imaging criteria of malignancy with a new diagnostic gold standard, namely, pathological analysis combined with cytogenetic and molecular genetic analyses. Nineteen patients with a fatty tumour were included. All had computed tomography and/or magnetic resonance imaging examination before any biopsy or surgery. All had histopathological and cytogenetic and/or molecular genetic analyses. The imaging diagnosis of benign or malignant lesions was accurate in 15 cases, with 4 false positives for malignancy. Erroneous criteria were a large size (4 cases), and a mass that was not purely fatty. In conclusion, the main pitfall for a false positive radiological diagnosis of liposarcoma is certainly a large-sized tumour. Cytogenetic and molecular genetic analyses contribute to the diagnosis and can be performed at the same time with a core biopsy.

  7. ROBUST-HYBRID GENETIC ALGORITHM FOR A FLOW-SHOP SCHEDULING PROBLEM (A Case Study at PT FSCM Manufacturing Indonesia

    Directory of Open Access Journals (Sweden)

    Johan Soewanda

    2007-01-01

    Full Text Available This paper discusses the application of Robust Hybrid Genetic Algorithm to solve a flow-shop scheduling problem. The proposed algorithm attempted to reach minimum makespan. PT. FSCM Manufacturing Indonesia Plant 4's case was used as a test case to evaluate the performance of the proposed algorithm. The proposed algorithm was compared to Ant Colony, Genetic-Tabu, Hybrid Genetic Algorithm, and the company's algorithm. We found that Robust Hybrid Genetic produces statistically better result than the company's, but the same as Ant Colony, Genetic-Tabu, and Hybrid Genetic. In addition, Robust Hybrid Genetic Algorithm required less computational time than Hybrid Genetic Algorithm

  8. Evolving aerodynamic airfoils for wind turbines through a genetic algorithm

    Science.gov (United States)

    Hernández, J. J.; Gómez, E.; Grageda, J. I.; Couder, C.; Solís, A.; Hanotel, C. L.; Ledesma, JI

    2017-01-01

    Nowadays, genetic algorithms stand out for airfoil optimisation, due to the virtues of mutation and crossing-over techniques. In this work we propose a genetic algorithm with arithmetic crossover rules. The optimisation criteria are taken to be the maximisation of both aerodynamic efficiency and lift coefficient, while minimising drag coefficient. Such algorithm shows greatly improvements in computational costs, as well as a high performance by obtaining optimised airfoils for Mexico City's specific wind conditions from generic wind turbines designed for higher Reynolds numbers, in few iterations.

  9. Applying natural evolution for solving computational problems - Lecture 1

    CERN Multimedia

    CERN. Geneva

    2017-01-01

    Darwin’s natural evolution theory has inspired computer scientists for solving computational problems. In a similar way to how humans and animals have evolved along millions of years, computational problems can be solved by evolving a population of solutions through generations until a good solution is found. In the first lecture, the fundaments of evolutionary computing (EC) will be described, covering the different phases that the evolutionary process implies. ECJ, a framework for researching in such field, will be also explained. In the second lecture, genetic programming (GP) will be covered. GP is a sub-field of EC where solutions are actual computational programs represented by trees. Bloat control and distributed evaluation will be introduced.

  10. Applying natural evolution for solving computational problems - Lecture 2

    CERN Multimedia

    CERN. Geneva

    2017-01-01

    Darwin’s natural evolution theory has inspired computer scientists for solving computational problems. In a similar way to how humans and animals have evolved along millions of years, computational problems can be solved by evolving a population of solutions through generations until a good solution is found. In the first lecture, the fundaments of evolutionary computing (EC) will be described, covering the different phases that the evolutionary process implies. ECJ, a framework for researching in such field, will be also explained. In the second lecture, genetic programming (GP) will be covered. GP is a sub-field of EC where solutions are actual computational programs represented by trees. Bloat control and distributed evaluation will be introduced.

  11. Evolutionary computing in Nuclear Engineering Institute/CNEN-Brazil

    International Nuclear Information System (INIS)

    Pereira, Claudio M.N.A.; Lapa, Celso M.F.; Lapa, Nelbia da Silva; Mol, Antonio C.

    2000-01-01

    This paper aims to discuss the importance of evolutionary computation (CE) for nuclear engineering and the development of this area in the Instituto de Engenharia Nuclear (IEN) at the last years. Are describe, briefly, the applications realized in this institute by the technical group of CE. For example: nuclear reactor core design optimization, preventive maintenance scheduling optimizing and nuclear reactor transient identifications. It is also shown a novel computational tool to implementation of genetic algorithm that was development in this institute and applied in those works. Some results were presents and the gains obtained with the evolutionary computation were discussing. (author)

  12. Mouse Genome Informatics (MGI) Resource: Genetic, Genomic, and Biological Knowledgebase for the Laboratory Mouse.

    Science.gov (United States)

    Eppig, Janan T

    2017-07-01

    The Mouse Genome Informatics (MGI) Resource supports basic, translational, and computational research by providing high-quality, integrated data on the genetics, genomics, and biology of the laboratory mouse. MGI serves a strategic role for the scientific community in facilitating biomedical, experimental, and computational studies investigating the genetics and processes of diseases and enabling the development and testing of new disease models and therapeutic interventions. This review describes the nexus of the body of growing genetic and biological data and the advances in computer technology in the late 1980s, including the World Wide Web, that together launched the beginnings of MGI. MGI develops and maintains a gold-standard resource that reflects the current state of knowledge, provides semantic and contextual data integration that fosters hypothesis testing, continually develops new and improved tools for searching and analysis, and partners with the scientific community to assure research data needs are met. Here we describe one slice of MGI relating to the development of community-wide large-scale mutagenesis and phenotyping projects and introduce ways to access and use these MGI data. References and links to additional MGI aspects are provided. © The Author 2017. Published by Oxford University Press.

  13. Fuzzy systems and soft computing in nuclear engineering

    International Nuclear Information System (INIS)

    Ruan, D.

    2000-01-01

    This book is an organized edited collection of twenty-one contributed chapters covering nuclear engineering applications of fuzzy systems, neural networks, genetic algorithms and other soft computing techniques. All chapters are either updated review or original contributions by leading researchers written exclusively for this volume. The volume highlights the advantages of applying fuzzy systems and soft computing in nuclear engineering, which can be viewed as complementary to traditional methods. As a result, fuzzy sets and soft computing provide a powerful tool for solving intricate problems pertaining in nuclear engineering. Each chapter of the book is self-contained and also indicates the future research direction on this topic of applications of fuzzy systems and soft computing in nuclear engineering. (orig.)

  14. 11th International Conference on Computer and Information Science

    CERN Document Server

    Computer and Information 2012

    2012-01-01

    The series "Studies in Computational Intelligence" (SCI) publishes new developments and advances in the various areas of computational intelligence – quickly and with a high quality. The intent is to cover the theory, applications, and design methods of computational intelligence, as embedded in the fields of engineering, computer science, physics and life science, as well as the methodologies behind them. The series contains monographs, lecture notes and edited volumes in computational intelligence spanning the areas of neural networks, connectionist systems, genetic algorithms, evolutionary computation, artificial intelligence, cellular automata, self-organizing systems, soft computing, fuzzy systems, and hybrid intelligent systems. Critical to both contributors and readers are the short publication time and world-wide distribution - this permits a rapid and broad dissemination of research results.   The purpose of the 11th IEEE/ACIS International Conference on Computer and Information Science (ICIS 2012...

  15. Nonlinear inversion of potential-field data using a hybrid-encoding genetic algorithm

    Science.gov (United States)

    Chen, C.; Xia, J.; Liu, J.; Feng, G.

    2006-01-01

    Using a genetic algorithm to solve an inverse problem of complex nonlinear geophysical equations is advantageous because it does not require computer gradients of models or "good" initial models. The multi-point search of a genetic algorithm makes it easier to find the globally optimal solution while avoiding falling into a local extremum. As is the case in other optimization approaches, the search efficiency for a genetic algorithm is vital in finding desired solutions successfully in a multi-dimensional model space. A binary-encoding genetic algorithm is hardly ever used to resolve an optimization problem such as a simple geophysical inversion with only three unknowns. The encoding mechanism, genetic operators, and population size of the genetic algorithm greatly affect search processes in the evolution. It is clear that improved operators and proper population size promote the convergence. Nevertheless, not all genetic operations perform perfectly while searching under either a uniform binary or a decimal encoding system. With the binary encoding mechanism, the crossover scheme may produce more new individuals than with the decimal encoding. On the other hand, the mutation scheme in a decimal encoding system will create new genes larger in scope than those in the binary encoding. This paper discusses approaches of exploiting the search potential of genetic operations in the two encoding systems and presents an approach with a hybrid-encoding mechanism, multi-point crossover, and dynamic population size for geophysical inversion. We present a method that is based on the routine in which the mutation operation is conducted in the decimal code and multi-point crossover operation in the binary code. The mix-encoding algorithm is called the hybrid-encoding genetic algorithm (HEGA). HEGA provides better genes with a higher probability by a mutation operator and improves genetic algorithms in resolving complicated geophysical inverse problems. Another significant

  16. Generation of Compliant Mechanisms using Hybrid Genetic Algorithm

    Science.gov (United States)

    Sharma, D.; Deb, K.

    2014-10-01

    Compliant mechanism is a single piece elastic structure which can deform to perform the assigned task. In this work, compliant mechanisms are evolved using a constraint based bi-objective optimization formulation which requires one user defined parameter ( η). This user defined parameter limits a gap between a desired path and an actual path traced by the compliant mechanism. The non-linear and discrete optimization problems are solved using the hybrid Genetic Algorithm (GA) wherein domain specific initialization, two-dimensional crossover operator and repairing techniques are adopted. A bit-wise local search method is used with elitist non-dominated sorting genetic algorithm to further refine the compliant mechanisms. Parallel computations are performed on the master-slave architecture to reduce the computation time. A parametric study is carried out for η value which suggests a range to evolve topologically different compliant mechanisms. The applied and boundary conditions to the compliant mechanisms are considered the variables that are evolved by the hybrid GA. The post-analysis of results unveils that the complaint mechanisms are always supported at unique location that can evolve the non-dominated solutions.

  17. The Imaging and Cognition Genetics Conference 2011, ICG 2011: A meeting of minds

    Directory of Open Access Journals (Sweden)

    Stephanie eLe Hellard

    2012-05-01

    Full Text Available In June 2011, seventy researchers from the disciplines of cognitive science, genetics, psychology, psychiatry, neurobiology and computer science gathered in Os, Norway, for the first Imaging and Cognition Genetics meeting. The aim of the conference was to discuss progress, enhance collaboration and maximise resource sharing within this new field. Here we summarise some of the major themes that emerged from ICG 2011.

  18. Darwinian Spacecraft: Soft Computing Strategies Breeding Better, Faster Cheaper

    Science.gov (United States)

    Noever, David A.; Baskaran, Subbiah

    1999-01-01

    Computers can create infinite lists of combinations to try to solve a particular problem, a process called "soft-computing." This process uses statistical comparables, neural networks, genetic algorithms, fuzzy variables in uncertain environments, and flexible machine learning to create a system which will allow spacecraft to increase robustness, and metric evaluation. These concepts will allow for the development of a spacecraft which will allow missions to be performed at lower costs.

  19. Legal constraints on genetic data processing in European grids

    NARCIS (Netherlands)

    Mouw, Evert; van't Noordende, Guido; van Kampen, Antoine H. C.; Louter, Baas; Santcroos, Mark; Olabarriaga, Silvia D.

    2012-01-01

    European laws on privacy and data security are not explicit about the storage and processing of genetic data. Especially whole-genome data is identifying and contains a lot of personal information. Is processing of such data allowed in computing grids? To find out, we looked at legal precedents in

  20. Concrete Plant Operations Optimization Using Combined Simulation and Genetic Algorithms

    NARCIS (Netherlands)

    Cao, Ming; Lu, Ming; Zhang, Jian-Ping

    2004-01-01

    This work presents a new approach for concrete plant operations optimization by combining a ready mixed concrete (RMC) production simulation tool (called HKCONSIM) with a genetic algorithm (GA) based optimization procedure. A revamped HKCONSIM computer system can be used to automate the simulation

  1. Crystal structure prediction of flexible molecules using parallel genetic algorithms with a standard force field.

    Science.gov (United States)

    Kim, Seonah; Orendt, Anita M; Ferraro, Marta B; Facelli, Julio C

    2009-10-01

    This article describes the application of our distributed computing framework for crystal structure prediction (CSP) the modified genetic algorithms for crystal and cluster prediction (MGAC), to predict the crystal structure of flexible molecules using the general Amber force field (GAFF) and the CHARMM program. The MGAC distributed computing framework includes a series of tightly integrated computer programs for generating the molecule's force field, sampling crystal structures using a distributed parallel genetic algorithm and local energy minimization of the structures followed by the classifying, sorting, and archiving of the most relevant structures. Our results indicate that the method can consistently find the experimentally known crystal structures of flexible molecules, but the number of missing structures and poor ranking observed in some crystals show the need for further improvement of the potential. Copyright 2009 Wiley Periodicals, Inc.

  2. Real coded genetic algorithm for fuzzy time series prediction

    Science.gov (United States)

    Jain, Shilpa; Bisht, Dinesh C. S.; Singh, Phool; Mathpal, Prakash C.

    2017-10-01

    Genetic Algorithm (GA) forms a subset of evolutionary computing, rapidly growing area of Artificial Intelligence (A.I.). Some variants of GA are binary GA, real GA, messy GA, micro GA, saw tooth GA, differential evolution GA. This research article presents a real coded GA for predicting enrollments of University of Alabama. Data of Alabama University is a fuzzy time series. Here, fuzzy logic is used to predict enrollments of Alabama University and genetic algorithm optimizes fuzzy intervals. Results are compared to other eminent author works and found satisfactory, and states that real coded GA are fast and accurate.

  3. Nature-inspired computing and optimization theory and applications

    CERN Document Server

    Yang, Xin-She; Nakamatsu, Kazumi

    2017-01-01

    The book provides readers with a snapshot of the state of the art in the field of nature-inspired computing and its application in optimization. The approach is mainly practice-oriented: each bio-inspired technique or algorithm is introduced together with one of its possible applications. Applications cover a wide range of real-world optimization problems: from feature selection and image enhancement to scheduling and dynamic resource management, from wireless sensor networks and wiring network diagnosis to sports training planning and gene expression, from topology control and morphological filters to nutritional meal design and antenna array design. There are a few theoretical chapters comparing different existing techniques, exploring the advantages of nature-inspired computing over other methods, and investigating the mixing time of genetic algorithms. The book also introduces a wide range of algorithms, including the ant colony optimization, the bat algorithm, genetic algorithms, the collision-based opti...

  4. Efficient computation of the joint probability of multiple inherited risk alleles from pedigree data.

    Science.gov (United States)

    Madsen, Thomas; Braun, Danielle; Peng, Gang; Parmigiani, Giovanni; Trippa, Lorenzo

    2018-06-25

    The Elston-Stewart peeling algorithm enables estimation of an individual's probability of harboring germline risk alleles based on pedigree data, and serves as the computational backbone of important genetic counseling tools. However, it remains limited to the analysis of risk alleles at a small number of genetic loci because its computing time grows exponentially with the number of loci considered. We propose a novel, approximate version of this algorithm, dubbed the peeling and paring algorithm, which scales polynomially in the number of loci. This allows extending peeling-based models to include many genetic loci. The algorithm creates a trade-off between accuracy and speed, and allows the user to control this trade-off. We provide exact bounds on the approximation error and evaluate it in realistic simulations. Results show that the loss of accuracy due to the approximation is negligible in important applications. This algorithm will improve genetic counseling tools by increasing the number of pathogenic risk alleles that can be addressed. To illustrate we create an extended five genes version of BRCAPRO, a widely used model for estimating the carrier probabilities of BRCA1 and BRCA2 risk alleles and assess its computational properties. © 2018 WILEY PERIODICALS, INC.

  5. High-throughput open source computational methods for genetics and genomics

    NARCIS (Netherlands)

    Prins, J.C.P.

    2015-01-01

    Biology is increasingly data driven by virtue of the development of high-throughput technologies, such as DNA and RNA sequencing. Computational biology and bioinformatics are scientific disciplines that cross-over between the disciplines of biology, informatics and statistics; which is clearly

  6. Optimality and stability of symmetric evolutionary games with applications in genetic selection.

    Science.gov (United States)

    Huang, Yuanyuan; Hao, Yiping; Wang, Min; Zhou, Wen; Wu, Zhijun

    2015-06-01

    Symmetric evolutionary games, i.e., evolutionary games with symmetric fitness matrices, have important applications in population genetics, where they can be used to model for example the selection and evolution of the genotypes of a given population. In this paper, we review the theory for obtaining optimal and stable strategies for symmetric evolutionary games, and provide some new proofs and computational methods. In particular, we review the relationship between the symmetric evolutionary game and the generalized knapsack problem, and discuss the first and second order necessary and sufficient conditions that can be derived from this relationship for testing the optimality and stability of the strategies. Some of the conditions are given in different forms from those in previous work and can be verified more efficiently. We also derive more efficient computational methods for the evaluation of the conditions than conventional approaches. We demonstrate how these conditions can be applied to justifying the strategies and their stabilities for a special class of genetic selection games including some in the study of genetic disorders.

  7. Computerised Genetic Risk Assessment and Decision Support in Primary Care

    Directory of Open Access Journals (Sweden)

    Andrew Coulson

    2000-09-01

    To address these issues, a new computer application called RAGs (Risk Assessment in Genetics has been designed. The system allows a doctor to create family trees and assess genetic risk of breast cancer. RAGs possesses two features that distinguish it from similar software: (a a user-centred design, which takes into account the requirements of the doctor-patient encounter; (b risk reporting using qualitative evidence for or against an increased risk, which the authors believe to be more useful and accessible than numerical probabilities are. In that the system allows for any genetic risk guideline to be implemented, it can be used with all diseases for which evaluation guidelines exist. The software may be easily modified to cater for the amount of detail required by different specialists.

  8. A weighted U statistic for association analyses considering genetic heterogeneity.

    Science.gov (United States)

    Wei, Changshuai; Elston, Robert C; Lu, Qing

    2016-07-20

    Converging evidence suggests that common complex diseases with the same or similar clinical manifestations could have different underlying genetic etiologies. While current research interests have shifted toward uncovering rare variants and structural variations predisposing to human diseases, the impact of heterogeneity in genetic studies of complex diseases has been largely overlooked. Most of the existing statistical methods assume the disease under investigation has a homogeneous genetic effect and could, therefore, have low power if the disease undergoes heterogeneous pathophysiological and etiological processes. In this paper, we propose a heterogeneity-weighted U (HWU) method for association analyses considering genetic heterogeneity. HWU can be applied to various types of phenotypes (e.g., binary and continuous) and is computationally efficient for high-dimensional genetic data. Through simulations, we showed the advantage of HWU when the underlying genetic etiology of a disease was heterogeneous, as well as the robustness of HWU against different model assumptions (e.g., phenotype distributions). Using HWU, we conducted a genome-wide analysis of nicotine dependence from the Study of Addiction: Genetics and Environments dataset. The genome-wide analysis of nearly one million genetic markers took 7h, identifying heterogeneous effects of two new genes (i.e., CYP3A5 and IKBKB) on nicotine dependence. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

  9. Genetic variations of robinia pseudoacacia plant using sds-page

    International Nuclear Information System (INIS)

    Zahoor, M.; Islam, N. U.; Nisar, M.

    2015-01-01

    The biochemical analysis using SDS-PAGE has great contribution for the estimation of genetic diversity. We estimated the genetic diversity of R. pseudoacacia germ plasm protein. A total of 19 varieties were collected from different areas of Dir lower were investigated for the level of genetic divergence and genetic linkages. The total germ plasm grouped were separated at 20 percentage distance into two linkages based on Euclidean distances the 19 cultivars were further divide at 45 percentage distance into three clusters, cluster 1, cluster 2 and cluster 3. Cluster 1 was comprised of Munda 3, Munda 4, Talash 2 and UOM 1. Cluster 2 was comprised of Maidan 1 and Gulabad 1. Cluster 3 was comprised Maidan 2, UOM 3, Talash 1, Maidan 4, Maidan 3, Gulabad 2, Gulabad 3 and Gulabad 4. A total of range 00 percentage to 88 percentage variation recoded among 19 varieties. The result obtained after SDS-PAGE were computed for the construction of phylogenetic diversity, geographic relationship, Euclidian distance, genetic distance and linkage distance. This plant show a lot of variation in germ plasmic level. It is concluded that it is possible to improve and produce new varieties of this plant. (author)

  10. Assessing individual risk for AMD with genetic counseling, family history, and genetic testing.

    Science.gov (United States)

    Cascella, R; Strafella, C; Longo, G; Manzo, L; Ragazzo, M; De Felici, C; Gambardella, S; Marsella, L T; Novelli, G; Borgiani, P; Sangiuolo, F; Cusumano, A; Ricci, F; Giardina, E

    2018-02-01

    PurposeThe goal was to develop a simple model for predicting the individual risk profile for age-related macular degeneration (AMD) on the basis of genetic information, disease family history, and smoking habits.Patients and methodsThe study enrolled 151 AMD patients following specific clinical and environmental inclusion criteria: age >55 years, positive family history for AMD, presence of at least one first-degree relative affected by AMD, and smoking habits. All of the samples were genotyped for rs1061170 (CFH) and rs10490924 (ARMS2) with a TaqMan assay, using a 7500 Fast Real Time PCR device. Statistical analysis was subsequently employed to calculate the real individual risk (OR) based on the genetic data (ORgn), family history (ORf), and smoking habits (ORsm).Results and conclusionThe combination of ORgn, ORf, and ORsm allowed the calculation of the Ort that represented the realistic individual risk for developing AMD. In this report, we present a computational model for the estimation of the individual risk for AMD. Moreover, we show that the average distribution of risk alleles in the general population and the knowledge of parents' genotype can be decisive to assess the real disease risk. In this contest, genetic counseling is crucial to provide the patients with an understanding of their individual risk and the availability for preventive actions.

  11. On Generating Optimal Signal Probabilities for Random Tests: A Genetic Approach

    Directory of Open Access Journals (Sweden)

    M. Srinivas

    1996-01-01

    Full Text Available Genetic Algorithms are robust search and optimization techniques. A Genetic Algorithm based approach for determining the optimal input distributions for generating random test vectors is proposed in the paper. A cost function based on the COP testability measure for determining the efficacy of the input distributions is discussed. A brief overview of Genetic Algorithms (GAs and the specific details of our implementation are described. Experimental results based on ISCAS-85 benchmark circuits are presented. The performance of our GAbased approach is compared with previous results. While the GA generates more efficient input distributions than the previous methods which are based on gradient descent search, the overheads of the GA in computing the input distributions are larger.

  12. International Conference on Frontiers of Intelligent Computing : Theory and Applications

    CERN Document Server

    Udgata, Siba; Biswal, Bhabendra

    2013-01-01

    The volume contains the papers presented at FICTA 2012: International Conference on Frontiers in Intelligent Computing: Theory and Applications held on December 22-23, 2012 in Bhubaneswar engineering College, Bhubaneswar, Odissa, India. It contains 86 papers contributed by authors from the globe. These research papers mainly focused on application of intelligent techniques which includes evolutionary computation techniques like genetic algorithm, particle swarm optimization techniques, teaching-learning based optimization etc  for various engineering applications such as data mining, image processing, cloud computing, networking etc.

  13. Biocontainment of genetically modified organisms by synthetic protein design

    Science.gov (United States)

    Mandell, Daniel J.; Lajoie, Marc J.; Mee, Michael T.; Takeuchi, Ryo; Kuznetsov, Gleb; Norville, Julie E.; Gregg, Christopher J.; Stoddard, Barry L.; Church, George M.

    2015-02-01

    Genetically modified organisms (GMOs) are increasingly deployed at large scales and in open environments. Genetic biocontainment strategies are needed to prevent unintended proliferation of GMOs in natural ecosystems. Existing biocontainment methods are insufficient because they impose evolutionary pressure on the organism to eject the safeguard by spontaneous mutagenesis or horizontal gene transfer, or because they can be circumvented by environmentally available compounds. Here we computationally redesign essential enzymes in the first organism possessing an altered genetic code (Escherichia coli strain C321.ΔA) to confer metabolic dependence on non-standard amino acids for survival. The resulting GMOs cannot metabolically bypass their biocontainment mechanisms using known environmental compounds, and they exhibit unprecedented resistance to evolutionary escape through mutagenesis and horizontal gene transfer. This work provides a foundation for safer GMOs that are isolated from natural ecosystems by a reliance on synthetic metabolites.

  14. Genetic algorithms used for PWRs refuel management automatic optimization: a new modelling

    International Nuclear Information System (INIS)

    Chapot, Jorge Luiz C.; Schirru, Roberto; Silva, Fernando Carvalho da

    1996-01-01

    A Genetic Algorithms-based system, linking the computer codes GENESIS 5.0 and ANC through the interface ALGER, has been developed aiming the PWRs fuel management optimization. An innovative codification, the Lists Model, has been incorporated to the genetic system, which avoids the use of variants of the standard crossover operator and generates only valid loading patterns in the core. The GENESIS/ALGER/ANC system has been successfully tested in an optimization study for Angra-1 second cycle. (author)

  15. Genetic classes and genetic categories : Protecting genetic groups through data protection law

    NARCIS (Netherlands)

    Hallinan, Dara; de Hert, Paul; Taylor, L.; Floridi, L.; van der Sloot, B.

    2017-01-01

    Each person shares genetic code with others. Thus, one individual’s genome can reveal information about other individuals. When multiple individuals share aspects of genetic architecture, they form a ‘genetic group’. From a social and legal perspective, two types of genetic group exist: Those which

  16. A Genetic algorithm for evaluating the zeros (roots) of polynomial ...

    African Journals Online (AJOL)

    This paper presents a Genetic Algorithm software (which is a computational, search technique) for finding the zeros (roots) of any given polynomial function, and optimizing and solving N-dimensional systems of equations. The software is particularly useful since most of the classic schemes are not all embracing.

  17. Plant Genetic Resources: Selected Issues from Genetic Erosion to Genetic Engineering

    Directory of Open Access Journals (Sweden)

    Karl Hammer

    2008-04-01

    Full Text Available Plant Genetic Resources (PGR continue to play an important role in the development of agriculture. The following aspects receive a special consideration:1. Definition. The term was coined in 1970. The genepool concept served as an important tool in the further development. Different approaches are discussed.2. Values of Genetic Resources. A short introduction is highlighting this problem and stressing the economic usfulness of PGR.3. Genetic Erosion. Already observed by E. Baur in 1914, this is now a key issue within PGR. The case studies cited include Ethiopia, Italy, China, S Korea, Greece and S. Africa. Modern approaches concentrate on allelic changes in varieties over time but neglect the landraces. The causes and consequences of genetic erosion are discussed.4. Genetic Resources Conservation. Because of genetic erosion there is a need for conservation. PGR should be consigned to the appropriate method of conservation (ex situ, in situ, on-farm according to the scientific basis of biodiversity (genetic diversity, species diversity, ecosystem diversity and the evolutionary status of plants (cultivated plants, weeds, related wild plants (crop wild relatives.5. GMO. The impact of genetically engineered plants on genetic diversity is discussed.6. The Conclusions and Recommendations stress the importance of PGR. Their conservation and use are urgent necessities for the present development and future survival of mankind.

  18. Genetic Network Programming with Reconstructed Individuals

    Science.gov (United States)

    Ye, Fengming; Mabu, Shingo; Wang, Lutao; Eto, Shinji; Hirasawa, Kotaro

    A lot of research on evolutionary computation has been done and some significant classical methods such as Genetic Algorithm (GA), Genetic Programming (GP), Evolutionary Programming (EP), and Evolution Strategies (ES) have been studied. Recently, a new approach named Genetic Network Programming (GNP) has been proposed. GNP can evolve itself and find the optimal solution. It is based on the idea of Genetic Algorithm and uses the data structure of directed graphs. Many papers have demonstrated that GNP can deal with complex problems in the dynamic environments very efficiently and effectively. As a result, recently, GNP is getting more and more attentions and is used in many different areas such as data mining, extracting trading rules of stock markets, elevator supervised control systems, etc., and GNP has obtained some outstanding results. Focusing on the GNP's distinguished expression ability of the graph structure, this paper proposes a method named Genetic Network Programming with Reconstructed Individuals (GNP-RI). The aim of GNP-RI is to balance the exploitation and exploration of GNP, that is, to strengthen the exploitation ability by using the exploited information extensively during the evolution process of GNP and finally obtain better performances than that of GNP. In the proposed method, the worse individuals are reconstructed and enhanced by the elite information before undergoing genetic operations (mutation and crossover). The enhancement of worse individuals mimics the maturing phenomenon in nature, where bad individuals can become smarter after receiving a good education. In this paper, GNP-RI is applied to the tile-world problem which is an excellent bench mark for evaluating the proposed architecture. The performance of GNP-RI is compared with that of the conventional GNP. The simulation results show some advantages of GNP-RI demonstrating its superiority over the conventional GNPs.

  19. Research on uranium resource models. Part IV. Logic: a computer graphics program to construct integrated logic circuits for genetic-geologic models. Progress report

    International Nuclear Information System (INIS)

    Scott, W.A.; Turner, R.M.; McCammon, R.B.

    1981-01-01

    Integrated logic circuits were described as a means of formally representing genetic-geologic models for estimating undiscovered uranium resources. The logic circuits are logical combinations of selected geologic characteristics judged to be associated with particular types of uranium deposits. Each combination takes on a value which corresponds to the combined presence, absence, or don't know states of the selected characteristic within a specified geographic cell. Within each cell, the output of the logic circuit is taken as a measure of the favorability of occurrence of an undiscovered deposit of the type being considered. In this way, geological, geochemical, and geophysical data are incorporated explicitly into potential uranium resource estimates. The present report describes how integrated logic circuits are constructed by use of a computer graphics program. A user's guide is also included

  20. Mapping the regional influence of genetics on brain structure variability--a tensor-based morphometry study.

    Science.gov (United States)

    Brun, Caroline C; Leporé, Natasha; Pennec, Xavier; Lee, Agatha D; Barysheva, Marina; Madsen, Sarah K; Avedissian, Christina; Chou, Yi-Yu; de Zubicaray, Greig I; McMahon, Katie L; Wright, Margaret J; Toga, Arthur W; Thompson, Paul M

    2009-10-15

    Genetic and environmental factors influence brain structure and function profoundly. The search for heritable anatomical features and their influencing genes would be accelerated with detailed 3D maps showing the degree to which brain morphometry is genetically determined. As part of an MRI study that will scan 1150 twins, we applied Tensor-Based Morphometry to compute morphometric differences in 23 pairs of identical twins and 23 pairs of same-sex fraternal twins (mean age: 23.8+/-1.8 SD years). All 92 twins' 3D brain MRI scans were nonlinearly registered to a common space using a Riemannian fluid-based warping approach to compute volumetric differences across subjects. A multi-template method was used to improve volume quantification. Vector fields driving each subject's anatomy onto the common template were analyzed to create maps of local volumetric excesses and deficits relative to the standard template. Using a new structural equation modeling method, we computed the voxelwise proportion of variance in volumes attributable to additive (A) or dominant (D) genetic factors versus shared environmental (C) or unique environmental factors (E). The method was also applied to various anatomical regions of interest (ROIs). As hypothesized, the overall volumes of the brain, basal ganglia, thalamus, and each lobe were under strong genetic control; local white matter volumes were mostly controlled by common environment. After adjusting for individual differences in overall brain scale, genetic influences were still relatively high in the corpus callosum and in early-maturing brain regions such as the occipital lobes, while environmental influences were greater in frontal brain regions that have a more protracted maturational time-course.

  1. Machine learning applications in genetics and genomics.

    Science.gov (United States)

    Libbrecht, Maxwell W; Noble, William Stafford

    2015-06-01

    The field of machine learning, which aims to develop computer algorithms that improve with experience, holds promise to enable computers to assist humans in the analysis of large, complex data sets. Here, we provide an overview of machine learning applications for the analysis of genome sequencing data sets, including the annotation of sequence elements and epigenetic, proteomic or metabolomic data. We present considerations and recurrent challenges in the application of supervised, semi-supervised and unsupervised machine learning methods, as well as of generative and discriminative modelling approaches. We provide general guidelines to assist in the selection of these machine learning methods and their practical application for the analysis of genetic and genomic data sets.

  2. Parallel island genetic algorithm applied to a nuclear power plant auxiliary feedwater system surveillance tests policy optimization

    International Nuclear Information System (INIS)

    Pereira, Claudio M.N.A.; Lapa, Celso M.F.

    2003-01-01

    In this work, we focus the application of an Island Genetic Algorithm (IGA), a coarse-grained parallel genetic algorithm (PGA) model, to a Nuclear Power Plant (NPP) Auxiliary Feedwater System (AFWS) surveillance tests policy optimization. Here, the main objective is to outline, by means of comparisons, the advantages of the IGA over the simple (non-parallel) genetic algorithm (GA), which has been successfully applied in the solution of such kind of problem. The goal of the optimization is to maximize the system's average availability for a given period of time, considering realistic features such as: i) aging effects on standby components during the tests; ii) revealing failures in the tests implies on corrective maintenance, increasing outage times; iii) components have distinct test parameters (outage time, aging factors, etc.) and iv) tests are not necessarily periodic. In our experiments, which were made in a cluster comprised by 8 1-GHz personal computers, we could clearly observe gains not only in the computational time, which reduced linearly with the number of computers, but in the optimization outcome

  3. CMCpy: Genetic Code-Message Coevolution Models in Python

    Science.gov (United States)

    Becich, Peter J.; Stark, Brian P.; Bhat, Harish S.; Ardell, David H.

    2013-01-01

    Code-message coevolution (CMC) models represent coevolution of a genetic code and a population of protein-coding genes (“messages”). Formally, CMC models are sets of quasispecies coupled together for fitness through a shared genetic code. Although CMC models display plausible explanations for the origin of multiple genetic code traits by natural selection, useful modern implementations of CMC models are not currently available. To meet this need we present CMCpy, an object-oriented Python API and command-line executable front-end that can reproduce all published results of CMC models. CMCpy implements multiple solvers for leading eigenpairs of quasispecies models. We also present novel analytical results that extend and generalize applications of perturbation theory to quasispecies models and pioneer the application of a homotopy method for quasispecies with non-unique maximally fit genotypes. Our results therefore facilitate the computational and analytical study of a variety of evolutionary systems. CMCpy is free open-source software available from http://pypi.python.org/pypi/CMCpy/. PMID:23532367

  4. Prediction of Software Reliability using Bio Inspired Soft Computing Techniques.

    Science.gov (United States)

    Diwaker, Chander; Tomar, Pradeep; Poonia, Ramesh C; Singh, Vijander

    2018-04-10

    A lot of models have been made for predicting software reliability. The reliability models are restricted to using particular types of methodologies and restricted number of parameters. There are a number of techniques and methodologies that may be used for reliability prediction. There is need to focus on parameters consideration while estimating reliability. The reliability of a system may increase or decreases depending on the selection of different parameters used. Thus there is need to identify factors that heavily affecting the reliability of the system. In present days, reusability is mostly used in the various area of research. Reusability is the basis of Component-Based System (CBS). The cost, time and human skill can be saved using Component-Based Software Engineering (CBSE) concepts. CBSE metrics may be used to assess those techniques which are more suitable for estimating system reliability. Soft computing is used for small as well as large-scale problems where it is difficult to find accurate results due to uncertainty or randomness. Several possibilities are available to apply soft computing techniques in medicine related problems. Clinical science of medicine using fuzzy-logic, neural network methodology significantly while basic science of medicine using neural-networks-genetic algorithm most frequently and preferably. There is unavoidable interest shown by medical scientists to use the various soft computing methodologies in genetics, physiology, radiology, cardiology and neurology discipline. CBSE boost users to reuse the past and existing software for making new products to provide quality with a saving of time, memory space, and money. This paper focused on assessment of commonly used soft computing technique like Genetic Algorithm (GA), Neural-Network (NN), Fuzzy Logic, Support Vector Machine (SVM), Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO), and Artificial Bee Colony (ABC). This paper presents working of soft computing

  5. International Conference on Frontiers of Intelligent Computing : Theory and Applications

    CERN Document Server

    Udgata, Siba; Biswal, Bhabendra

    2014-01-01

    This volume contains the papers presented at the Second International Conference on Frontiers in Intelligent Computing: Theory and Applications (FICTA-2013) held during 14-16 November 2013 organized by Bhubaneswar Engineering College (BEC), Bhubaneswar, Odisha, India. It contains 63 papers focusing on application of intelligent techniques which includes evolutionary computation techniques like genetic algorithm, particle swarm optimization techniques, teaching-learning based optimization etc  for various engineering applications such as data mining, Fuzzy systems, Machine Intelligence and ANN, Web technologies and Multimedia applications and Intelligent computing and Networking etc.

  6. Automatic Creation of Machine Learning Workflows with Strongly Typed Genetic Programming

    Czech Academy of Sciences Publication Activity Database

    Křen, T.; Pilát, M.; Neruda, Roman

    2017-01-01

    Roč. 26, č. 5 (2017), č. článku 1760020. ISSN 0218-2130 R&D Projects: GA ČR GA15-19877S Grant - others:GA MŠk(CZ) LM2015042 Institutional support: RVO:67985807 Keywords : genetic programming * machine learning workflows * asynchronous evolutionary algorithm Subject RIV: IN - Informatics, Computer Science OBOR OECD: Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8) Impact factor: 0.778, year: 2016

  7. Meeting review. Uncovering the genetic basis of adaptive change: on the intersection of landscape genomics and theoretical population genetics.

    Science.gov (United States)

    Joost, Stéphane; Vuilleumier, Séverine; Jensen, Jeffrey D; Schoville, Sean; Leempoel, Kevin; Stucki, Sylvie; Widmer, Ivo; Melodelima, Christelle; Rolland, Jonathan; Manel, Stéphanie

    2013-07-01

    A workshop recently held at the École Polytechnique Fédérale de Lausanne (EPFL, Switzerland) was dedicated to understanding the genetic basis of adaptive change, taking stock of the different approaches developed in theoretical population genetics and landscape genomics and bringing together knowledge accumulated in both research fields. Indeed, an important challenge in theoretical population genetics is to incorporate effects of demographic history and population structure. But important design problems (e.g. focus on populations as units, focus on hard selective sweeps, no hypothesis-based framework in the design of the statistical tests) reduce their capability of detecting adaptive genetic variation. In parallel, landscape genomics offers a solution to several of these problems and provides a number of advantages (e.g. fast computation, landscape heterogeneity integration). But the approach makes several implicit assumptions that should be carefully considered (e.g. selection has had enough time to create a functional relationship between the allele distribution and the environmental variable, or this functional relationship is assumed to be constant). To address the respective strengths and weaknesses mentioned above, the workshop brought together a panel of experts from both disciplines to present their work and discuss the relevance of combining these approaches, possibly resulting in a joint software solution in the future.

  8. Biotechnology Computing: Information Science for the Era of Molecular Medicine.

    Science.gov (United States)

    Masys, Daniel R.

    1989-01-01

    The evolution from classical genetics to biotechnology, an area of research involving key macromolecules in living cells, is chronicled and the current state of biotechnology is described, noting related advances in computing and clinical medicine. (MSE)

  9. A steady-State Genetic Algorithm with Resampling for Noisy Inventory Control

    NARCIS (Netherlands)

    Prestwich, S.; Tarim, S.A.; Rossi, R.; Hnich, B.

    2008-01-01

    Noisy fitness functions occur in many practical applications of evolutionary computation. A standard technique for solving these problems is fitness resampling but this may be inefficient or need a large population, and combined with elitism it may overvalue chromosomes or reduce genetic diversity.

  10. Tumour necrosis factor alpha (TNF-α) genetic polymorphisms and ...

    Indian Academy of Sciences (India)

    Sensitivity analysis of the summary odds ratio coefficients on the association between TNF-α-308G/A polymorphism and AILD risk using a random effects model. (A allele vs G allele). Results were computed by omitting each study in turn. Error bars are 95% confidence interval. Journal of Genetics, Vol. 92, No. 3, December ...

  11. Adults' perceptions of genetic counseling and genetic testing.

    Science.gov (United States)

    Houfek, Julia Fisco; Soltis-Vaughan, Brigette S; Atwood, Jan R; Reiser, Gwendolyn M; Schaefer, G Bradley

    2015-02-01

    This study described the perceptions of genetic counseling and testing of adults (N = 116) attending a genetic education program. Understanding perceptions of genetic counseling, including the importance of counseling topics, will contribute to patient-focused care as clinical genetic applications for common, complex disorders evolve. Participants completed a survey addressing: the importance of genetic counseling topics, benefits and negative effects of genetic testing, and sharing test results. Topics addressing practical information about genetic conditions were rated most important; topics involving conceptual genetic/genomic principles were rated least important. The most frequently identified benefit and negative effect of testing were prevention/early detection/treatment and psychological distress. Participants perceived that they were more likely to share test results with first-degree than other relatives. Findings suggest providing patients with practical information about genetic testing and genetic contributions to disease, while also determining whether their self-care abilities would be enhanced by teaching genetic/genomic principles. Copyright © 2014 Elsevier Inc. All rights reserved.

  12. Optimizing Fuzzy Rule Base for Illumination Compensation in Face Recognition using Genetic Algorithms

    Directory of Open Access Journals (Sweden)

    Bima Sena Bayu Dewantara

    2014-12-01

    Full Text Available Fuzzy rule optimization is a challenging step in the development of a fuzzy model. A simple two inputs fuzzy model may have thousands of combination of fuzzy rules when it deals with large number of input variations. Intuitively and trial‐error determination of fuzzy rule is very difficult. This paper addresses the problem of optimizing Fuzzy rule using Genetic Algorithm to compensate illumination effect in face recognition. Since uneven illumination contributes negative effects to the performance of face recognition, those effects must be compensated. We have developed a novel algorithmbased on a reflectance model to compensate the effect of illumination for human face recognition. We build a pair of model from a single image and reason those modelsusing Fuzzy.Fuzzy rule, then, is optimized using Genetic Algorithm. This approachspendsless computation cost by still keepinga high performance. Based on the experimental result, we can show that our algorithm is feasiblefor recognizing desired person under variable lighting conditions with faster computation time. Keywords: Face recognition, harsh illumination, reflectance model, fuzzy, genetic algorithm

  13. Genetic demographic networks: Mathematical model and applications.

    Science.gov (United States)

    Kimmel, Marek; Wojdyła, Tomasz

    2016-10-01

    Recent improvement in the quality of genetic data obtained from extinct human populations and their ancestors encourages searching for answers to basic questions regarding human population history. The most common and successful are model-based approaches, in which genetic data are compared to the data obtained from the assumed demography model. Using such approach, it is possible to either validate or adjust assumed demography. Model fit to data can be obtained based on reverse-time coalescent simulations or forward-time simulations. In this paper we introduce a computational method based on mathematical equation that allows obtaining joint distributions of pairs of individuals under a specified demography model, each of them characterized by a genetic variant at a chosen locus. The two individuals are randomly sampled from either the same or two different populations. The model assumes three types of demographic events (split, merge and migration). Populations evolve according to the time-continuous Moran model with drift and Markov-process mutation. This latter process is described by the Lyapunov-type equation introduced by O'Brien and generalized in our previous works. Application of this equation constitutes an original contribution. In the result section of the paper we present sample applications of our model to both simulated and literature-based demographies. Among other we include a study of the Slavs-Balts-Finns genetic relationship, in which we model split and migrations between the Balts and Slavs. We also include another example that involves the migration rates between farmers and hunters-gatherers, based on modern and ancient DNA samples. This latter process was previously studied using coalescent simulations. Our results are in general agreement with the previous method, which provides validation of our approach. Although our model is not an alternative to simulation methods in the practical sense, it provides an algorithm to compute pairwise

  14. Bacterial computing: a form of natural computing and its applications.

    Science.gov (United States)

    Lahoz-Beltra, Rafael; Navarro, Jorge; Marijuán, Pedro C

    2014-01-01

    The capability to establish adaptive relationships with the environment is an essential characteristic of living cells. Both bacterial computing and bacterial intelligence are two general traits manifested along adaptive behaviors that respond to surrounding environmental conditions. These two traits have generated a variety of theoretical and applied approaches. Since the different systems of bacterial signaling and the different ways of genetic change are better known and more carefully explored, the whole adaptive possibilities of bacteria may be studied under new angles. For instance, there appear instances of molecular "learning" along the mechanisms of evolution. More in concrete, and looking specifically at the time dimension, the bacterial mechanisms of learning and evolution appear as two different and related mechanisms for adaptation to the environment; in somatic time the former and in evolutionary time the latter. In the present chapter it will be reviewed the possible application of both kinds of mechanisms to prokaryotic molecular computing schemes as well as to the solution of real world problems.

  15. Challenges and opportunities in estimating viral genetic diversity from next-generation sequencing data

    Directory of Open Access Journals (Sweden)

    Niko eBeerenwinkel

    2012-09-01

    Full Text Available Many viruses, including the clinically relevant RNA viruses HIV and HCV, exist in large populations and display high genetic heterogeneity within and between infected hosts. Assessing intra-patient viral genetic diversity is essential for understanding the evolutionary dynamics of viruses, for designing effective vaccines, and for the success of antiviral therapy. Next-generation sequencing technologies allow the rapid and cost-effective acquisition of thousands to millions of short DNA sequences from a single sample. However, this approach entails several challenges in experimental design and computational data analysis. Here, we review the entire process of inferring viral diversity from sample collection to computing measures of genetic diversity. We discuss sample preparation, including reverse transcription and amplification, and the effect of experimental conditions on diversity estimates due to in vitro base substitutions, insertions, deletions, and recombination. The use of different next-generation sequencing platforms and their sequencing error profiles are compared in the context of various applications of diversity estimation, ranging from the detection of single nucleotide variants to the reconstruction of whole-genome haplotypes. We describe the statistical and computational challenges arising from these technical artifacts, and we review existing approaches, including available software, for their solution. Finally, we discuss open problems, and highlight successful biomedical applications and potential future clinical use of next-generation sequencing to estimate viral diversity.

  16. Privacy, the individual and genetic information: a Buddhist perspective.

    Science.gov (United States)

    Hongladarom, Soraj

    2009-09-01

    Bioinformatics is a new field of study whose ethical implications involve a combination of bioethics, computer ethics and information ethics. This paper is an attempt to view some of these implications from the perspective of Buddhism. Privacy is a central concern in both computer/information ethics and bioethics, and with information technology being increasingly utilized to process biological and genetic data, the issue has become even more pronounced. Traditionally, privacy presupposes the individual self but as Buddhism does away with the ultimate conception of an individual self, it has to find a way to analyse and justify privacy that does not presuppose such a self. It does this through a pragmatic conception that does not depend on a positing of the substantial self, which is then found to be unnecessary for an effective protection of privacy. As it may be possible one day to link genetic data to individuals, the Buddhist conception perhaps offers a more flexible approach, as what is considered to be integral to an individual person is not fixed in objectivity but depends on convention.

  17. Approximate Bayesian computation.

    Directory of Open Access Journals (Sweden)

    Mikael Sunnåker

    Full Text Available Approximate Bayesian computation (ABC constitutes a class of computational methods rooted in Bayesian statistics. In all model-based statistical inference, the likelihood function is of central importance, since it expresses the probability of the observed data under a particular statistical model, and thus quantifies the support data lend to particular values of parameters and to choices among different models. For simple models, an analytical formula for the likelihood function can typically be derived. However, for more complex models, an analytical formula might be elusive or the likelihood function might be computationally very costly to evaluate. ABC methods bypass the evaluation of the likelihood function. In this way, ABC methods widen the realm of models for which statistical inference can be considered. ABC methods are mathematically well-founded, but they inevitably make assumptions and approximations whose impact needs to be carefully assessed. Furthermore, the wider application domain of ABC exacerbates the challenges of parameter estimation and model selection. ABC has rapidly gained popularity over the last years and in particular for the analysis of complex problems arising in biological sciences (e.g., in population genetics, ecology, epidemiology, and systems biology.

  18. Genetic Algorithm for Traveling Salesman Problem with Modified Cycle Crossover Operator

    Directory of Open Access Journals (Sweden)

    Abid Hussain

    2017-01-01

    Full Text Available Genetic algorithms are evolutionary techniques used for optimization purposes according to survival of the fittest idea. These methods do not ensure optimal solutions; however, they give good approximation usually in time. The genetic algorithms are useful for NP-hard problems, especially the traveling salesman problem. The genetic algorithm depends on selection criteria, crossover, and mutation operators. To tackle the traveling salesman problem using genetic algorithms, there are various representations such as binary, path, adjacency, ordinal, and matrix representations. In this article, we propose a new crossover operator for traveling salesman problem to minimize the total distance. This approach has been linked with path representation, which is the most natural way to represent a legal tour. Computational results are also reported with some traditional path representation methods like partially mapped and order crossovers along with new cycle crossover operator for some benchmark TSPLIB instances and found improvements.

  19. Mechanisms and impact of genetic recombination in the evolution of Streptococcus pneumoniae.

    Science.gov (United States)

    Chaguza, Chrispin; Cornick, Jennifer E; Everett, Dean B

    2015-01-01

    Streptococcus pneumoniae (the pneumococcus) is a highly recombinogenic bacterium responsible for a high burden of human disease globally. Genetic recombination, a process in which exogenous DNA is acquired and incorporated into its genome, is a key evolutionary mechanism employed by the pneumococcus to rapidly adapt to selective pressures. The rate at which the pneumococcus acquires genetic variation through recombination is much higher than the rate at which the organism acquires variation through spontaneous mutations. This higher rate of variation allows the pneumococcus to circumvent the host innate and adaptive immune responses, escape clinical interventions, including antibiotic therapy and vaccine introduction. The rapid influx of whole genome sequence (WGS) data and the advent of novel analysis methods and powerful computational tools for population genetics and evolution studies has transformed our understanding of how genetic recombination drives pneumococcal adaptation and evolution. Here we discuss how genetic recombination has impacted upon the evolution of the pneumococcus.

  20. Towards a genetic architecture of cryptic genetic variation

    Indian Academy of Sciences (India)

    Home; Journals; Journal of Genetics; Volume 84; Issue 3. Towards a genetic architecture of cryptic genetic variation and genetic assimilation: the contribution of K. G. Bateman. Ian Dworkin. Commentary on J. Genet. Classic Volume 84 Issue 3 December 2005 pp 223-226 ...

  1. Machine learning patterns for neuroimaging-genetic studies in the cloud.

    Science.gov (United States)

    Da Mota, Benoit; Tudoran, Radu; Costan, Alexandru; Varoquaux, Gaël; Brasche, Goetz; Conrod, Patricia; Lemaitre, Herve; Paus, Tomas; Rietschel, Marcella; Frouin, Vincent; Poline, Jean-Baptiste; Antoniu, Gabriel; Thirion, Bertrand

    2014-01-01

    Brain imaging is a natural intermediate phenotype to understand the link between genetic information and behavior or brain pathologies risk factors. Massive efforts have been made in the last few years to acquire high-dimensional neuroimaging and genetic data on large cohorts of subjects. The statistical analysis of such data is carried out with increasingly sophisticated techniques and represents a great computational challenge. Fortunately, increasing computational power in distributed architectures can be harnessed, if new neuroinformatics infrastructures are designed and training to use these new tools is provided. Combining a MapReduce framework (TomusBLOB) with machine learning algorithms (Scikit-learn library), we design a scalable analysis tool that can deal with non-parametric statistics on high-dimensional data. End-users describe the statistical procedure to perform and can then test the model on their own computers before running the very same code in the cloud at a larger scale. We illustrate the potential of our approach on real data with an experiment showing how the functional signal in subcortical brain regions can be significantly fit with genome-wide genotypes. This experiment demonstrates the scalability and the reliability of our framework in the cloud with a 2 weeks deployment on hundreds of virtual machines.

  2. Genetic Algorithms for Multiple-Choice Problems

    Science.gov (United States)

    Aickelin, Uwe

    2010-04-01

    This thesis investigates the use of problem-specific knowledge to enhance a genetic algorithm approach to multiple-choice optimisation problems.It shows that such information can significantly enhance performance, but that the choice of information and the way it is included are important factors for success.Two multiple-choice problems are considered.The first is constructing a feasible nurse roster that considers as many requests as possible.In the second problem, shops are allocated to locations in a mall subject to constraints and maximising the overall income.Genetic algorithms are chosen for their well-known robustness and ability to solve large and complex discrete optimisation problems.However, a survey of the literature reveals room for further research into generic ways to include constraints into a genetic algorithm framework.Hence, the main theme of this work is to balance feasibility and cost of solutions.In particular, co-operative co-evolution with hierarchical sub-populations, problem structure exploiting repair schemes and indirect genetic algorithms with self-adjusting decoder functions are identified as promising approaches.The research starts by applying standard genetic algorithms to the problems and explaining the failure of such approaches due to epistasis.To overcome this, problem-specific information is added in a variety of ways, some of which are designed to increase the number of feasible solutions found whilst others are intended to improve the quality of such solutions.As well as a theoretical discussion as to the underlying reasons for using each operator,extensive computational experiments are carried out on a variety of data.These show that the indirect approach relies less on problem structure and hence is easier to implement and superior in solution quality.

  3. Genetic algorithms - A new technique for solving the neutron spectrum unfolding problem

    International Nuclear Information System (INIS)

    Freeman, David W.; Edwards, D. Ray; Bolon, Albert E.

    1999-01-01

    A new technique utilizing genetic algorithms has been applied to the Bonner sphere neutron spectrum unfolding problem. Genetic algorithms are part of a relatively new field of 'evolutionary' solution techniques that mimic living systems with computer-simulated 'chromosome' solutions. Solutions mate and mutate to create better solutions. Several benchmark problems, considered representative of radiation protection environments, have been evaluated using the newly developed UMRGA code which implements the genetic algorithm unfolding technique. The results are compared with results from other well-established unfolding codes. The genetic algorithm technique works remarkably well and produces solutions with relatively high spectral qualities. UMRGA appears to be a superior technique in the absence of a priori data - it does not rely on 'lucky' guesses of input spectra. Calculated personnel doses associated with the unfolded spectra match benchmark values within a few percent

  4. Genetic similarity of soybean genotypes revealed by seed protein

    Directory of Open Access Journals (Sweden)

    Nikolić Ana

    2005-01-01

    Full Text Available More accurate and complete descriptions of genotypes could help determinate future breeding strategies and facilitate introgression of new genotypes in current soybean genetic pool. The objective of this study was to characterize 20 soybean genotypes from the Maize Research Institute "Zemun Polje" collection, which have good agronomic performances, high yield, lodging and drought resistance, and low shuttering by seed proteins as biochemical markers. Seed proteins were isolated and separated by PAA electrophoresis. On the basis of the presence/absence of protein fractions coefficients of similarity were calculated as Dice and Roger and Tanamoto coefficient between pairs of genotypes. The similarity matrix was submitted for hierarchical cluster analysis of un weighted pair group using arithmetic average (UPGMA method and necessary computation were performed using NTSYS-pc program. Protein seed analysis confirmed low level of genetic diversity in soybean. The highest genetic similarity was between genotypes P9272 and Kador. According to obtained results, soybean genotypes were assigned in two larger groups and coefficients of similarity showed similar results. Because of the lack of pedigree data for analyzed genotypes, correspondence with marker data could not be determined. In plant with a narrow genetic base in their gene pool, such as soybean, protein markers may not be sufficient for characterization and study of genetic diversity.

  5. Genetic privacy.

    Science.gov (United States)

    Sankar, Pamela

    2003-01-01

    During the past 10 years, the number of genetic tests performed more than tripled, and public concern about genetic privacy emerged. The majority of states and the U.S. government have passed regulations protecting genetic information. However, research has shown that concerns about genetic privacy are disproportionate to known instances of information misuse. Beliefs in genetic determinacy explain some of the heightened concern about genetic privacy. Discussion of the debate over genetic testing within families illustrates the most recent response to genetic privacy concerns.

  6. A Genetic-Based Feature Selection Approach in the Identification of Left/Right Hand Motor Imagery for a Brain-Computer Interface

    Directory of Open Access Journals (Sweden)

    Charles Yaacoub

    2017-01-01

    Full Text Available Electroencephalography is a non-invasive measure of the brain electrical activity generated by millions of neurons. Feature extraction in electroencephalography analysis is a core issue that may lead to accurate brain mental state classification. This paper presents a new feature selection method that improves left/right hand movement identification of a motor imagery brain-computer interface, based on genetic algorithms and artificial neural networks used as classifiers. Raw electroencephalography signals are first preprocessed using appropriate filtering. Feature extraction is carried out afterwards, based on spectral and temporal signal components, and thus a feature vector is constructed. As various features might be inaccurate and mislead the classifier, thus degrading the overall system performance, the proposed approach identifies a subset of features from a large feature space, such that the classifier error rate is reduced. Experimental results show that the proposed method is able to reduce the number of features to as low as 0.5% (i.e., the number of ignored features can reach 99.5% while improving the accuracy, sensitivity, specificity, and precision of the classifier.

  7. A Genetic-Based Feature Selection Approach in the Identification of Left/Right Hand Motor Imagery for a Brain-Computer Interface.

    Science.gov (United States)

    Yaacoub, Charles; Mhanna, Georges; Rihana, Sandy

    2017-01-23

    Electroencephalography is a non-invasive measure of the brain electrical activity generated by millions of neurons. Feature extraction in electroencephalography analysis is a core issue that may lead to accurate brain mental state classification. This paper presents a new feature selection method that improves left/right hand movement identification of a motor imagery brain-computer interface, based on genetic algorithms and artificial neural networks used as classifiers. Raw electroencephalography signals are first preprocessed using appropriate filtering. Feature extraction is carried out afterwards, based on spectral and temporal signal components, and thus a feature vector is constructed. As various features might be inaccurate and mislead the classifier, thus degrading the overall system performance, the proposed approach identifies a subset of features from a large feature space, such that the classifier error rate is reduced. Experimental results show that the proposed method is able to reduce the number of features to as low as 0.5% (i.e., the number of ignored features can reach 99.5%) while improving the accuracy, sensitivity, specificity, and precision of the classifier.

  8. Determinatıon of Some Genetic Parameters, Phenotypic, Genetic and Environmental Trends and Environmental Factors Affecting Milk Yield Traits of Brown Swiss Cattle

    Directory of Open Access Journals (Sweden)

    Muhammet Hanifi Selvi

    2016-01-01

    Full Text Available In this study, genetic parameters, macro environmental factors and genetic, phenotypic and environmental trends for actual and 305 day milk yield of Brown Swiss cattle reared in Research Farm of Agricultural College at Atatürk University were estimated. Estimated breeding values that were used for calculation of the genetic trend and genetic parameters were estimated by using MTDFREML computer package program. Environmental factors affecting on actual and 305day milk yields were analysed by using Harvey statistic package program. While effects of the years and parities on the actual and 305-day milk yields were highly significant, the influence of the calving season was found to be insignificant. Environmental and phenotypic trends for actual and 305-day milk yields were determined as -33.2 kg and -29.0 kg; and -27.8±19.1 kg/year and -25.9±8.7 kg/year respectively. Genetic trends for actual and 305-day milk yields were calculated as 5.4±3.8 kg and 3.1±3.4 kg. Heritability’s for actual and 305-day milk yields were 0.21±0.12 and 0.16±0.14 respectively. Repeatability values for actual and 305-day milk yield were found as 0.29 and 0.33 respectively.

  9. Estimation of genetic connectedness diagnostics based on prediction errors without the prediction error variance-covariance matrix.

    Science.gov (United States)

    Holmes, John B; Dodds, Ken G; Lee, Michael A

    2017-03-02

    An important issue in genetic evaluation is the comparability of random effects (breeding values), particularly between pairs of animals in different contemporary groups. This is usually referred to as genetic connectedness. While various measures of connectedness have been proposed in the literature, there is general agreement that the most appropriate measure is some function of the prediction error variance-covariance matrix. However, obtaining the prediction error variance-covariance matrix is computationally demanding for large-scale genetic evaluations. Many alternative statistics have been proposed that avoid the computational cost of obtaining the prediction error variance-covariance matrix, such as counts of genetic links between contemporary groups, gene flow matrices, and functions of the variance-covariance matrix of estimated contemporary group fixed effects. In this paper, we show that a correction to the variance-covariance matrix of estimated contemporary group fixed effects will produce the exact prediction error variance-covariance matrix averaged by contemporary group for univariate models in the presence of single or multiple fixed effects and one random effect. We demonstrate the correction for a series of models and show that approximations to the prediction error matrix based solely on the variance-covariance matrix of estimated contemporary group fixed effects are inappropriate in certain circumstances. Our method allows for the calculation of a connectedness measure based on the prediction error variance-covariance matrix by calculating only the variance-covariance matrix of estimated fixed effects. Since the number of fixed effects in genetic evaluation is usually orders of magnitudes smaller than the number of random effect levels, the computational requirements for our method should be reduced.

  10. A new paradigm of knowledge engineering by soft computing

    CERN Document Server

    Ding, Liya

    2001-01-01

    Soft computing (SC) consists of several computing paradigms, including neural networks, fuzzy set theory, approximate reasoning, and derivative-free optimization methods such as genetic algorithms. The integration of those constituent methodologies forms the core of SC. In addition, the synergy allows SC to incorporate human knowledge effectively, deal with imprecision and uncertainty, and learn to adapt to unknown or changing environments for better performance. Together with other modern technologies, SC and its applications exert unprecedented influence on intelligent systems that mimic hum

  11. Genetic and Computational Approaches for Studying Plant Development and Abiotic Stress Responses Using Image-Based Phenotyping

    Science.gov (United States)

    Campbell, M. T.; Walia, H.; Grondin, A.; Knecht, A.

    2017-12-01

    The development of abiotic stress tolerant crops (i.e. drought, salinity, or heat stress) requires the discovery of DNA sequence variants associated with stress tolerance-related traits. However, many traits underlying adaptation to abiotic stress involve a suite of physiological pathways that may be induced at different times throughout the duration of stress. Conventional single-point phenotyping approaches fail to fully capture these temporal responses, and thus downstream genetic analysis may only identify a subset of the genetic variants that are important for adaptation to sub-optimal environments. Although genomic resources for crops have advanced tremendously, the collection of phenotypic data for morphological and physiological traits is laborious and remains a significant bottleneck in bridging the phenotype-genotype gap. In recent years, the availability of automated, image-based phenotyping platforms has provided researchers with an opportunity to collect morphological and physiological traits non-destructively in a highly controlled environment. Moreover, these platforms allow abiotic stress responses to be recorded throughout the duration of the experiment, and have facilitated the use of function-valued traits for genetic analyses in major crops. We will present our approaches for addressing abiotic stress tolerance in cereals. This talk will focus on novel open-source software to process and extract biological meaningful data from images generated from these phenomics platforms. In addition, we will discuss the statistical approaches to model longitudinal phenotypes and dissect the genetic basis of dynamic responses to these abiotic stresses throughout development.

  12. Profiling bacterial kinase activity using a genetic circuit

    DEFF Research Database (Denmark)

    van der Helm, Eric; Bech, Rasmus; Lehning, Christina Eva

    Phosphorylation is a post-translational modification that regulates the activity of several key proteins in bacteria and eukaryotes. Accordingly, a variety of tools has been developed to measure kinase activity. To couple phosphorylation to an in vivo fluorescent readout we used the Bacillus...... subtilis kinase PtkA, transmembrane activator TkmA and the repressor FatR to construct a genetic circuit in E. coli. By tuning the repressor and kinase expression level at the same time, we were able to show a 4.2-fold increase in signal upon kinase induction. We furthermore validated that the previously...... reported FatR Y45E mutation1 attenuates operator repression. This genetic circuit provides a starting point for computational protein design and a metagenomic library-screening tool....

  13. The heterogeneous HLA genetic makeup of the Swiss population.

    Science.gov (United States)

    Buhler, Stéphane; Nunes, José Manuel; Nicoloso, Grazia; Tiercy, Jean-Marie; Sanchez-Mazas, Alicia

    2012-01-01

    This study aims at investigating the HLA molecular variation across Switzerland in order to determine possible regional differences, which would be highly relevant to several purposes: optimizing donor recruitment strategies in hematopoietic stem cell transplantation (HSCT), providing reliable reference data in HLA and disease association studies, and understanding the population genetic background(s) of this culturally heterogeneous country. HLA molecular data of more than 20,000 HSCT donors from 9-13 recruitment centers of the whole country were analyzed. Allele and haplotype frequencies were estimated by using new computer tools adapted to the heterogeneity and ambiguity of the data. Non-parametric and resampling statistical tests were performed to assess Hardy-Weinberg equilibrium, selective neutrality and linkage disequilibrium among different loci, both in each recruitment center and in the whole national registry. Genetic variation was explored through genetic distance and hierarchical analysis of variance taking into account both geographic and linguistic subdivisions in Switzerland. The results indicate a heterogeneous genetic makeup of the Swiss population: first, allele frequencies estimated on the whole national registry strongly deviate from Hardy-Weinberg equilibrium, by contrast with the results obtained for individual centers; second, a pronounced differentiation is observed for Ticino, Graubünden, and, to a lesser extent, Wallis, suggesting that the Alps represent(ed) a barrier to gene flow; finally, although cultural (linguistic) boundaries do not represent a main genetic differentiation factor in Switzerland, the genetic relatedness between population from south-eastern Switzerland and Italy agrees with historical and linguistic data. Overall, this study justifies the maintenance of a decentralized donor recruitment structure in Switzerland allowing increasing the genetic diversity of the national--and hence global--donor registry. It also

  14. Efficient multiple-trait association and estimation of genetic correlation using the matrix-variate linear mixed model.

    Science.gov (United States)

    Furlotte, Nicholas A; Eskin, Eleazar

    2015-05-01

    Multiple-trait association mapping, in which multiple traits are used simultaneously in the identification of genetic variants affecting those traits, has recently attracted interest. One class of approaches for this problem builds on classical variance component methodology, utilizing a multitrait version of a linear mixed model. These approaches both increase power and provide insights into the genetic architecture of multiple traits. In particular, it is possible to estimate the genetic correlation, which is a measure of the portion of the total correlation between traits that is due to additive genetic effects. Unfortunately, the practical utility of these methods is limited since they are computationally intractable for large sample sizes. In this article, we introduce a reformulation of the multiple-trait association mapping approach by defining the matrix-variate linear mixed model. Our approach reduces the computational time necessary to perform maximum-likelihood inference in a multiple-trait model by utilizing a data transformation. By utilizing a well-studied human cohort, we show that our approach provides more than a 10-fold speedup, making multiple-trait association feasible in a large population cohort on the genome-wide scale. We take advantage of the efficiency of our approach to analyze gene expression data. By decomposing gene coexpression into a genetic and environmental component, we show that our method provides fundamental insights into the nature of coexpressed genes. An implementation of this method is available at http://genetics.cs.ucla.edu/mvLMM. Copyright © 2015 by the Genetics Society of America.

  15. Genome-Wide Association Study of the Genetic Determinants of Emphysema Distribution.

    Science.gov (United States)

    Boueiz, Adel; Lutz, Sharon M; Cho, Michael H; Hersh, Craig P; Bowler, Russell P; Washko, George R; Halper-Stromberg, Eitan; Bakke, Per; Gulsvik, Amund; Laird, Nan M; Beaty, Terri H; Coxson, Harvey O; Crapo, James D; Silverman, Edwin K; Castaldi, Peter J; DeMeo, Dawn L

    2017-03-15

    Emphysema has considerable variability in the severity and distribution of parenchymal destruction throughout the lungs. Upper lobe-predominant emphysema has emerged as an important predictor of response to lung volume reduction surgery. Yet, aside from alpha-1 antitrypsin deficiency, the genetic determinants of emphysema distribution remain largely unknown. To identify the genetic influences of emphysema distribution in non-alpha-1 antitrypsin-deficient smokers. A total of 11,532 subjects with complete genotype and computed tomography densitometry data in the COPDGene (Genetic Epidemiology of Chronic Obstructive Pulmonary Disease [COPD]; non-Hispanic white and African American), ECLIPSE (Evaluation of COPD Longitudinally to Identify Predictive Surrogate Endpoints), and GenKOLS (Genetics of Chronic Obstructive Lung Disease) studies were analyzed. Two computed tomography scan emphysema distribution measures (difference between upper-third and lower-third emphysema; ratio of upper-third to lower-third emphysema) were tested for genetic associations in all study subjects. Separate analyses in each study population were followed by a fixed effect metaanalysis. Single-nucleotide polymorphism-, gene-, and pathway-based approaches were used. In silico functional evaluation was also performed. We identified five loci associated with emphysema distribution at genome-wide significance. These loci included two previously reported associations with COPD susceptibility (4q31 near HHIP and 15q25 near CHRNA5) and three new associations near SOWAHB, TRAPPC9, and KIAA1462. Gene set analysis and in silico functional evaluation revealed pathways and cell types that may potentially contribute to the pathogenesis of emphysema distribution. This multicohort genome-wide association study identified new genomic loci associated with differential emphysematous destruction throughout the lungs. These findings may point to new biologic pathways on which to expand diagnostic and therapeutic

  16. Computational intelligence in digital forensics forensic investigation and applications

    CERN Document Server

    Choo, Yun-Huoy; Abraham, Ajith; Srihari, Sargur

    2014-01-01

    Computational Intelligence techniques have been widely explored in various domains including forensics. Analysis in forensic encompasses the study of pattern analysis that answer the question of interest in security, medical, legal, genetic studies and etc. However, forensic analysis is usually performed through experiments in lab which is expensive both in cost and time. Therefore, this book seeks to explore the progress and advancement of computational intelligence technique in different focus areas of forensic studies. This aims to build stronger connection between computer scientists and forensic field experts.   This book, Computational Intelligence in Digital Forensics: Forensic Investigation and Applications, is the first volume in the Intelligent Systems Reference Library series. The book presents original research results and innovative applications of computational intelligence in digital forensics. This edited volume contains seventeen chapters and presents the latest state-of-the-art advancement ...

  17. A novel low-parameter computational model to aid in-silico glycoengineering

    DEFF Research Database (Denmark)

    Spahn, Philipp N.; Hansen, Anders Holmgaard; Hansen, Henning Gram

    2015-01-01

    benefit from computational models that would better meet the requirements for industrial utilization. Here, we introduce a novel approach combining constraints-based and stochastic techniques to derive a computational model that can predict the effects of gene knockouts on protein glycoprofiles while...... it does not follow any direct equivalent of a genetic code. Instead, its complex biogenesis in the Golgi apparatus (Figure 1A) integrates a variety of influencing factors most of which are only incompletely understood. Various attempts have been undertaken so far to computationally model the process...

  18. Automatic Data Filter Customization Using a Genetic Algorithm

    Science.gov (United States)

    Mandrake, Lukas

    2013-01-01

    This work predicts whether a retrieval algorithm will usefully determine CO2 concentration from an input spectrum of GOSAT (Greenhouse Gases Observing Satellite). This was done to eliminate needless runtime on atmospheric soundings that would never yield useful results. A space of 50 dimensions was examined for predictive power on the final CO2 results. Retrieval algorithms are frequently expensive to run, and wasted effort defeats requirements and expends needless resources. This algorithm could be used to help predict and filter unneeded runs in any computationally expensive regime. Traditional methods such as the Fischer discriminant analysis and decision trees can attempt to predict whether a sounding will be properly processed. However, this work sought to detect a subsection of the dimensional space that can be simply filtered out to eliminate unwanted runs. LDAs (linear discriminant analyses) and other systems examine the entire data and judge a "best fit," giving equal weight to complex and problematic regions as well as simple, clear-cut regions. In this implementation, a genetic space of "left" and "right" thresholds outside of which all data are rejected was defined. These left/right pairs are created for each of the 50 input dimensions. A genetic algorithm then runs through countless potential filter settings using a JPL computer cluster, optimizing the tossed-out data s yield (proper vs. improper run removal) and number of points tossed. This solution is robust to an arbitrary decision boundary within the data and avoids the global optimization problem of whole-dataset fitting using LDA or decision trees. It filters out runs that would not have produced useful CO2 values to save needless computation. This would be an algorithmic preprocessing improvement to any computationally expensive system.

  19. Tree Based Decision Strategies and Auctions in Computational Multi-Agent Systems

    Czech Academy of Sciences Publication Activity Database

    Šlapák, M.; Neruda, Roman

    2017-01-01

    Roč. 38, č. 4 (2017), s. 335-342 ISSN 0257-4306 Institutional support: RVO:67985807 Keywords : auction systems * decision making * genetic programming * multi-agent system * task distribution Subject RIV: IN - Informatics, Computer Science OBOR OECD: Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8) http://rev-inv-ope.univ-paris1.fr/fileadmin/rev-inv-ope/files/38417/38417-04.pdf

  20. Genetic Constructor: An Online DNA Design Platform.

    Science.gov (United States)

    Bates, Maxwell; Lachoff, Joe; Meech, Duncan; Zulkower, Valentin; Moisy, Anaïs; Luo, Yisha; Tekotte, Hille; Franziska Scheitz, Cornelia Johanna; Khilari, Rupal; Mazzoldi, Florencio; Chandran, Deepak; Groban, Eli

    2017-12-15

    Genetic Constructor is a cloud Computer Aided Design (CAD) application developed to support synthetic biologists from design intent through DNA fabrication and experiment iteration. The platform allows users to design, manage, and navigate complex DNA constructs and libraries, using a new visual language that focuses on functional parts abstracted from sequence. Features like combinatorial libraries and automated primer design allow the user to separate design from construction by focusing on functional intent, and design constraints aid iterative refinement of designs. A plugin architecture enables contributions from scientists and coders to leverage existing powerful software and connect to DNA foundries. The software is easily accessible and platform agnostic, free for academics, and available in an open-source community edition. Genetic Constructor seeks to democratize DNA design, manufacture, and access to tools and services from the synthetic biology community.

  1. A computationally efficient fuzzy control s

    Directory of Open Access Journals (Sweden)

    Abdel Badie Sharkawy

    2013-12-01

    Full Text Available This paper develops a decentralized fuzzy control scheme for MIMO nonlinear second order systems with application to robot manipulators via a combination of genetic algorithms (GAs and fuzzy systems. The controller for each degree of freedom (DOF consists of a feedforward fuzzy torque computing system and a feedback fuzzy PD system. The feedforward fuzzy system is trained and optimized off-line using GAs, whereas not only the parameters but also the structure of the fuzzy system is optimized. The feedback fuzzy PD system, on the other hand, is used to keep the closed-loop stable. The rule base consists of only four rules per each DOF. Furthermore, the fuzzy feedback system is decentralized and simplified leading to a computationally efficient control scheme. The proposed control scheme has the following advantages: (1 it needs no exact dynamics of the system and the computation is time-saving because of the simple structure of the fuzzy systems and (2 the controller is robust against various parameters and payload uncertainties. The computational complexity of the proposed control scheme has been analyzed and compared with previous works. Computer simulations show that this controller is effective in achieving the control goals.

  2. Genetic algorithms applied to the nuclear power plant operation

    International Nuclear Information System (INIS)

    Schirru, R.; Martinez, A.S.; Pereira, C.M.N.A.

    2000-01-01

    Nuclear power plant operation often involves very important human decisions, such as actions to be taken after a nuclear accident/transient, or finding the best core reload pattern, a complex combinatorial optimization problem which requires expert knowledge. Due to the complexity involved in the decisions to be taken, computerized systems have been intensely explored in order to aid the operator. Following hardware advances, soft computing has been improved and, nowadays, intelligent technologies, such as genetic algorithms, neural networks and fuzzy systems, are being used to support operator decisions. In this chapter two main problems are explored: transient diagnosis and nuclear core refueling. Here, solutions to such kind of problems, based on genetic algorithms, are described. A genetic algorithm was designed to optimize the nuclear fuel reload of Angra-1 nuclear power plant. Results compared to those obtained by an expert reveal a gain in the burn-up cycle. Two other genetic algorithm approaches were used to optimize real time diagnosis systems. The first one learns partitions in the time series that represents the transients, generating a set of classification centroids. The other one involves the optimization of an adaptive vector quantization neural network. Results are shown and commented. (orig.)

  3. Fog computing job scheduling optimization based on bees swarm

    Science.gov (United States)

    Bitam, Salim; Zeadally, Sherali; Mellouk, Abdelhamid

    2018-04-01

    Fog computing is a new computing architecture, composed of a set of near-user edge devices called fog nodes, which collaborate together in order to perform computational services such as running applications, storing an important amount of data, and transmitting messages. Fog computing extends cloud computing by deploying digital resources at the premise of mobile users. In this new paradigm, management and operating functions, such as job scheduling aim at providing high-performance, cost-effective services requested by mobile users and executed by fog nodes. We propose a new bio-inspired optimization approach called Bees Life Algorithm (BLA) aimed at addressing the job scheduling problem in the fog computing environment. Our proposed approach is based on the optimized distribution of a set of tasks among all the fog computing nodes. The objective is to find an optimal tradeoff between CPU execution time and allocated memory required by fog computing services established by mobile users. Our empirical performance evaluation results demonstrate that the proposal outperforms the traditional particle swarm optimization and genetic algorithm in terms of CPU execution time and allocated memory.

  4. Novel opportunities for computational biology and sociology in drug discovery☆

    Science.gov (United States)

    Yao, Lixia; Evans, James A.; Rzhetsky, Andrey

    2013-01-01

    Current drug discovery is impossible without sophisticated modeling and computation. In this review we outline previous advances in computational biology and, by tracing the steps involved in pharmaceutical development, explore a range of novel, high-value opportunities for computational innovation in modeling the biological process of disease and the social process of drug discovery. These opportunities include text mining for new drug leads, modeling molecular pathways and predicting the efficacy of drug cocktails, analyzing genetic overlap between diseases and predicting alternative drug use. Computation can also be used to model research teams and innovative regions and to estimate the value of academy–industry links for scientific and human benefit. Attention to these opportunities could promise punctuated advance and will complement the well-established computational work on which drug discovery currently relies. PMID:20349528

  5. Novel opportunities for computational biology and sociology in drug discovery

    Science.gov (United States)

    Yao, Lixia

    2009-01-01

    Drug discovery today is impossible without sophisticated modeling and computation. In this review we touch on previous advances in computational biology and by tracing the steps involved in pharmaceutical development, we explore a range of novel, high value opportunities for computational innovation in modeling the biological process of disease and the social process of drug discovery. These opportunities include text mining for new drug leads, modeling molecular pathways and predicting the efficacy of drug cocktails, analyzing genetic overlap between diseases and predicting alternative drug use. Computation can also be used to model research teams and innovative regions and to estimate the value of academy-industry ties for scientific and human benefit. Attention to these opportunities could promise punctuated advance, and will complement the well-established computational work on which drug discovery currently relies. PMID:19674801

  6. 1st International Conference on Computational and Experimental Biomedical Sciences

    CERN Document Server

    Jorge, RM

    2015-01-01

    This book contains the full papers presented at ICCEBS 2013 – the 1st International Conference on Computational and Experimental Biomedical Sciences, which was organized in Azores, in October 2013. The included papers present and discuss new trends in those fields, using several methods and techniques, including active shape models, constitutive models, isogeometric elements, genetic algorithms, level sets, material models, neural networks, optimization, and the finite element method, in order to address more efficiently different and timely applications involving biofluids, computer simulation, computational biomechanics, image based diagnosis, image processing and analysis, image segmentation, image registration, scaffolds, simulation, and surgical planning. The main audience for this book consists of researchers, Ph.D students, and graduate students with multidisciplinary interests related to the areas of artificial intelligence, bioengineering, biology, biomechanics, computational fluid dynamics, comput...

  7. EggLib: processing, analysis and simulation tools for population genetics and genomics

    Directory of Open Access Journals (Sweden)

    De Mita Stéphane

    2012-04-01

    Full Text Available Abstract Background With the considerable growth of available nucleotide sequence data over the last decade, integrated and flexible analytical tools have become a necessity. In particular, in the field of population genetics, there is a strong need for automated and reliable procedures to conduct repeatable and rapid polymorphism analyses, coalescent simulations, data manipulation and estimation of demographic parameters under a variety of scenarios. Results In this context, we present EggLib (Evolutionary Genetics and Genomics Library, a flexible and powerful C++/Python software package providing efficient and easy to use computational tools for sequence data management and extensive population genetic analyses on nucleotide sequence data. EggLib is a multifaceted project involving several integrated modules: an underlying computationally efficient C++ library (which can be used independently in pure C++ applications; two C++ programs; a Python package providing, among other features, a high level Python interface to the C++ library; and the egglib script which provides direct access to pre-programmed Python applications. Conclusions EggLib has been designed aiming to be both efficient and easy to use. A wide array of methods are implemented, including file format conversion, sequence alignment edition, coalescent simulations, neutrality tests and estimation of demographic parameters by Approximate Bayesian Computation (ABC. Classes implementing different demographic scenarios for ABC analyses can easily be developed by the user and included to the package. EggLib source code is distributed freely under the GNU General Public License (GPL from its website http://egglib.sourceforge.net/ where a full documentation and a manual can also be found and downloaded.

  8. EggLib: processing, analysis and simulation tools for population genetics and genomics.

    Science.gov (United States)

    De Mita, Stéphane; Siol, Mathieu

    2012-04-11

    With the considerable growth of available nucleotide sequence data over the last decade, integrated and flexible analytical tools have become a necessity. In particular, in the field of population genetics, there is a strong need for automated and reliable procedures to conduct repeatable and rapid polymorphism analyses, coalescent simulations, data manipulation and estimation of demographic parameters under a variety of scenarios. In this context, we present EggLib (Evolutionary Genetics and Genomics Library), a flexible and powerful C++/Python software package providing efficient and easy to use computational tools for sequence data management and extensive population genetic analyses on nucleotide sequence data. EggLib is a multifaceted project involving several integrated modules: an underlying computationally efficient C++ library (which can be used independently in pure C++ applications); two C++ programs; a Python package providing, among other features, a high level Python interface to the C++ library; and the egglib script which provides direct access to pre-programmed Python applications. EggLib has been designed aiming to be both efficient and easy to use. A wide array of methods are implemented, including file format conversion, sequence alignment edition, coalescent simulations, neutrality tests and estimation of demographic parameters by Approximate Bayesian Computation (ABC). Classes implementing different demographic scenarios for ABC analyses can easily be developed by the user and included to the package. EggLib source code is distributed freely under the GNU General Public License (GPL) from its website http://egglib.sourceforge.net/ where a full documentation and a manual can also be found and downloaded.

  9. Optimal recombination in genetic algorithms for combinatorial optimization problems: Part II

    Directory of Open Access Journals (Sweden)

    Eremeev Anton V.

    2014-01-01

    Full Text Available This paper surveys results on complexity of the optimal recombination problem (ORP, which consists in finding the best possible offspring as a result of a recombination operator in a genetic algorithm, given two parent solutions. In Part II, we consider the computational complexity of ORPs arising in genetic algorithms for problems on permutations: the Travelling Salesman Problem, the Shortest Hamilton Path Problem and the Makespan Minimization on Single Machine and some other related problems. The analysis indicates that the corresponding ORPs are NP-hard, but solvable by faster algorithms, compared to the problems they are derived from.

  10. About Genetic Counselors

    Science.gov (United States)

    ... clinical care in many areas of medicine. Assisted Reproductive Technology/Infertility Genetics Cancer Genetics Cardiovascular Genetics Cystic Fibrosis Genetics Fetal Intervention and Therapy Genetics Hematology Genetics Metabolic Genetics ...

  11. Fourth International Conference on Computer Science and Its Applications (CIIA 2013)

    CERN Document Server

    Mohamed, Otmane; Bellatreche, Ladjel; Recent Advances in Robotics and Automation

    2013-01-01

        "During the last decades Computational Intelligence has emerged and showed its contributions in various broad research communities (computer science, engineering, finance, economic, decision making, etc.). This was done by proposing approaches and algorithms based either on turnkey techniques belonging to the large panoply of solutions offered by computational intelligence such as data mining, genetic algorithms, bio-inspired methods, Bayesian networks, machine learning, fuzzy logic, artificial neural networks, etc. or inspired by computational intelligence techniques to develop new ad-hoc algorithms for the problem under consideration.    This volume is a comprehensive collection of extended contributions from the 4th International Conference on Computer Science and Its Applications (CIIA’2013) organized into four main tracks: Track 1: Computational Intelligence, Track  2: Security & Network Technologies, Track  3: Information Technology and Track 4: Computer Systems and Applications. This ...

  12. Variability of individual genetic load: consequences for the detection of inbreeding depression.

    Science.gov (United States)

    Restoux, Gwendal; Huot de Longchamp, Priscille; Fady, Bruno; Klein, Etienne K

    2012-03-01

    Inbreeding depression is a key factor affecting the persistence of natural populations, particularly when they are fragmented. In species with mixed mating systems, inbreeding depression can be estimated at the population level by regressing the average progeny fitness by the selfing rate of their mothers. We applied this method using simulated populations to investigate how population genetic parameters can affect the detection power of inbreeding depression. We simulated individual selfing rates and genetic loads from which we computed fitness values. The regression method yielded high statistical power, inbreeding depression being detected as significant (5 % level) in 92 % of the simulations. High individual variation in selfing rate and high mean genetic load led to better detection of inbreeding depression while high among-individual variation in genetic load made it more difficult to detect inbreeding depression. For a constant sampling effort, increasing the number of progenies while decreasing the number of individuals per progeny enhanced the detection power of inbreeding depression. We discuss the implication of among-mother variability of genetic load and selfing rate on inbreeding depression studies.

  13. Generic Machine Learning Pattern for Neuroimaging-Genetic Studies in the Cloud

    Directory of Open Access Journals (Sweden)

    Benoit eDa Mota

    2014-04-01

    Full Text Available Brain imaging is a natural intermediate phenotype to understand the link between genetic information and behavior or brain pathologies risk factors. Massive efforts have been made in the last few years to acquire high-dimensional neuroimaging and genetic data on large cohorts of subjects. The statistical analysis of such data is carried out with increasingly sophisticated techniques and represents a great computational challenge. Fortunately, increasing computational power in distributed architectures can be harnessed, if new neuroinformatics infrastructures are designed and training to use these new tools is provided. Combining a MapReduce framework (TomusBLOB with machine learning algorithms (Scikit-learn library, we design a scalable analysis tool that can deal with non-parametric statistics on high-dimensional data. End-users describe the statistical procedure to perform and can then test the model on their own computers before running the very same code in the cloud at a larger scale. We illustrate the potential of our approach on real data with an experiment showing how the functional signal in subcortical brain regions can be significantly fit with genome-wide genotypes. This experiment demonstrates the scalability and the reliability of our framework in the cloud with a two weeks deployment on hundreds of virtual machines.

  14. A statistical assessment of differences and equivalences between genetically modified and reference plant varieties

    NARCIS (Netherlands)

    Voet, van der H.; Perry, J.N.; Amzal, B.; Paoletti, C.

    2011-01-01

    Background - Safety assessment of genetically modified organisms is currently often performed by comparative evaluation. However, natural variation of plant characteristics between commercial varieties is usually not considered explicitly in the statistical computations underlying the assessment.

  15. A comparative phylogenetic study of genetics and folk music.

    Science.gov (United States)

    Pamjav, Horolma; Juhász, Zoltán; Zalán, Andrea; Németh, Endre; Damdin, Bayarlkhagva

    2012-04-01

    Computer-aided comparison of folk music from different nations is one of the newest research areas. We were intrigued to have identified some important similarities between phylogenetic studies and modern folk music. First of all, both of them use similar concepts and representation tools such as multidimensional scaling for modelling relationship between populations. This gave us the idea to investigate whether these connections are merely accidental or if they mirror population migrations from the past. We raised the question; does the complex structure of musical connections display a clear picture and can this system be interpreted by the genetic analysis? This study is the first to systematically investigate the incidental genetic background of the folk music context between different populations. Paternal (42 populations) and maternal lineages (56 populations) were compared based on Fst genetic distances of the Y chromosomal and mtDNA haplogroup frequencies. To test this hypothesis, the corresponding musical cultures were also compared using an automatic overlap analysis of parallel melody styles for 31 Eurasian nations. We found that close musical relations of populations indicate close genetic distances (music; maternal lineages have a more important role in folk music traditions than paternal lineages. Furthermore, the combination of these disciplines establishing a new interdisciplinary research field of "music-genetics" can be an efficient tool to get a more comprehensive picture on the complex behaviour of populations in prehistoric time.

  16. A simulation study of mutations in the genetic regulatory hierarchy for butterfly eyespot focus determination.

    Science.gov (United States)

    Marcus, Jeffrey M; Evans, Travis M

    2008-09-01

    The color patterns on the wings of butterflies have been an important model system in evolutionary developmental biology. A recent computational model tested genetic regulatory hierarchies hypothesized to underlie the formation of butterfly eyespot foci [Evans, T.M., Marcus, J.M., 2006. A simulation study of the genetic regulatory hierarchy for butterfly eyespot focus determination. Evol. Dev. 8, 273-283]. The computational model demonstrated that one proposed hierarchy was incapable of reproducing the known patterns of gene expression associated with eyespot focus determination in wild-type butterflies, but that two slightly modified alternative hierarchies were capable of reproducing all of the known gene expressions patterns. Here we extend the computational models previously implemented in Delphi 2.0 to two mutants derived from the squinting bush brown butterfly (Bicyclus anynana). These two mutants, comet and Cyclops, have aberrantly shaped eyespot foci that are produced by different mechanisms. The comet mutation appears to produce a modified interaction between the wing margin and the eyespot focus that results in a series of comet-shaped eyespot foci. The Cyclops mutation causes the failure of wing vein formation between two adjacent wing-cells and the fusion of two adjacent eyespot foci to form a single large elongated focus in their place. The computational approach to modeling pattern formation in these mutants allows us to make predictions about patterns of gene expression, which are largely unstudied in butterfly mutants. It also suggests a critical experiment that will allow us to distinguish between two hypothesized genetic regulatory hierarchies that may underlie all butterfly eyespot foci.

  17. Unscented Sampling Techniques For Evolutionary Computation With Applications To Astrodynamic Optimization

    Science.gov (United States)

    2016-09-01

    factors that can cause the variations in trajectory computation time. First of all, these cases are initially computed using the guess-free mode of DIDO... Goldberg [91]. This concept essentially states that fundamental building blocks, or lower order schemata are pieced together by the genetic algorithms in...in Section 3.13.2. While this idea is very straightforward and logical, Goldberg also later points out that there are deceptive problems where these

  18. Parallel genetic algorithm as a tool for nuclear reactors reload

    International Nuclear Information System (INIS)

    Santos, Darley Roberto G.; Schirru, Roberto

    1999-01-01

    This work intends to present a tool which can be used by designers in order to get better solutions, in terms of computational costs, to solve problems of nuclear reactor reloads. It is known that the project of nuclear fuel reload is a complex combinatorial one. Generally, iterative processes are the most used ones because they generate answers to satisfy all restrictions. The model presented here uses Artificial Intelligence techniques, more precisely Genetic Algorithms techniques, mixed with parallelization techniques.Test of the tool presented here were highly satisfactory, due to a considerable reduction in computational time. (author)

  19. Bacterial computing: a form of natural computing and its applications

    Directory of Open Access Journals (Sweden)

    Rafael eLahoz-Beltra

    2014-03-01

    Full Text Available The capability to establish adaptive relationships with the environment is an essential characteristic of living cells. Both bacterial computing and bacterial intelligence are two general traits manifested along adaptive behaviors that respond to surrounding environmental conditions. These two traits have generated a variety of theoretical and applied approaches. Since the different systems of bacterial signaling and the different ways of genetic change are better known and more carefully explored, the whole adaptive possibilities of bacteria may be studied under new angles. For instance, there appear instances of molecular learning along the mechanisms of evolution. More in concrete, and looking specifically at the time dimension, the bacterial mechanisms of learning and evolution appear as two different and related mechanisms for adaptation to the environment; in somatic time the former and in evolutionary time the latter. In the present chapter it will be reviewed the possible application of both kinds of mechanisms to prokaryotic molecular computing schemes as well as to the solution of real world problems.

  20. Application of genetic algorithms to in-core nuclear fuel management optimization

    International Nuclear Information System (INIS)

    Poon, P.W.; Parks, G.T.

    1993-01-01

    The search for an optimal arrangement of fresh and burnt fuel and control material within the core of a PWR represents a formidable optimization problem. The approach of combining the robust optimization capabilities of the Simulated Annealing (SA) algorithm with the computational speed of a Generalized Perturbation Theory (GPT) based evaluation methodology in the code FORMOSA has proved to be very effective. In this paper, we show that the incorporation of another stochastic search technique, a Genetic Algorithm, results in comparable optimization performance on serial computers and offers substantially superior performance on parallel machines. (orig.)

  1. Programmable full-adder computations in communicating three-dimensional cell cultures.

    Science.gov (United States)

    Ausländer, David; Ausländer, Simon; Pierrat, Xavier; Hellmann, Leon; Rachid, Leila; Fussenegger, Martin

    2018-01-01

    Synthetic biologists have advanced the design of trigger-inducible gene switches and their assembly into input-programmable circuits that enable engineered human cells to perform arithmetic calculations reminiscent of electronic circuits. By designing a versatile plug-and-play molecular-computation platform, we have engineered nine different cell populations with genetic programs, each of which encodes a defined computational instruction. When assembled into 3D cultures, these engineered cell consortia execute programmable multicellular full-adder logics in response to three trigger compounds.

  2. The parallel processing impact in the optimization of the reactors neutronic by genetic algorithms

    International Nuclear Information System (INIS)

    Pereira, Claudio M.N.A.; Universidade Federal, Rio de Janeiro, RJ; Lapa, Celso M.F.; Mol, Antonio C.A.

    2002-01-01

    Nowadays, many optimization problems found in nuclear engineering has been solved through genetic algorithms (GA). The robustness of such methods is strongly related to the nature of search process which is based on populations of solution candidates, and this fact implies high computational cost in the optimization process. The use of GA become more critical when the evaluation process of a solution candidate is highly time consuming. Problems of this nature are common in the nuclear engineering, and an example is the reactor design optimization, where neutronic codes, which consume high CPU time, must be run. Aiming to investigate the impact of the use of parallel computation in the solution, through GA, of a reactor design optimization problem, a parallel genetic algorithm (PGA), using the Island Model, was developed. Exhaustive experiments, then 1500 processing hours in 550 MHz personal computers, have been done, in order to compare the conventional GA with the PGA. Such experiments have demonstrating the superiority of the PGA not only in terms of execution time, but also, in the optimization results. (author)

  3. Genetic algorithms

    Science.gov (United States)

    Wang, Lui; Bayer, Steven E.

    1991-01-01

    Genetic algorithms are mathematical, highly parallel, adaptive search procedures (i.e., problem solving methods) based loosely on the processes of natural genetics and Darwinian survival of the fittest. Basic genetic algorithms concepts are introduced, genetic algorithm applications are introduced, and results are presented from a project to develop a software tool that will enable the widespread use of genetic algorithm technology.

  4. Optical character recognition systems for different languages with soft computing

    CERN Document Server

    Chaudhuri, Arindam; Badelia, Pratixa; K Ghosh, Soumya

    2017-01-01

    The book offers a comprehensive survey of soft-computing models for optical character recognition systems. The various techniques, including fuzzy and rough sets, artificial neural networks and genetic algorithms, are tested using real texts written in different languages, such as English, French, German, Latin, Hindi and Gujrati, which have been extracted by publicly available datasets. The simulation studies, which are reported in details here, show that soft-computing based modeling of OCR systems performs consistently better than traditional models. Mainly intended as state-of-the-art survey for postgraduates and researchers in pattern recognition, optical character recognition and soft computing, this book will be useful for professionals in computer vision and image processing alike, dealing with different issues related to optical character recognition.

  5. Evolutionary Computation and Its Applications in Neural and Fuzzy Systems

    Directory of Open Access Journals (Sweden)

    Biaobiao Zhang

    2011-01-01

    Full Text Available Neural networks and fuzzy systems are two soft-computing paradigms for system modelling. Adapting a neural or fuzzy system requires to solve two optimization problems: structural optimization and parametric optimization. Structural optimization is a discrete optimization problem which is very hard to solve using conventional optimization techniques. Parametric optimization can be solved using conventional optimization techniques, but the solution may be easily trapped at a bad local optimum. Evolutionary computation is a general-purpose stochastic global optimization approach under the universally accepted neo-Darwinian paradigm, which is a combination of the classical Darwinian evolutionary theory, the selectionism of Weismann, and the genetics of Mendel. Evolutionary algorithms are a major approach to adaptation and optimization. In this paper, we first introduce evolutionary algorithms with emphasis on genetic algorithms and evolutionary strategies. Other evolutionary algorithms such as genetic programming, evolutionary programming, particle swarm optimization, immune algorithm, and ant colony optimization are also described. Some topics pertaining to evolutionary algorithms are also discussed, and a comparison between evolutionary algorithms and simulated annealing is made. Finally, the application of EAs to the learning of neural networks as well as to the structural and parametric adaptations of fuzzy systems is also detailed.

  6. Fashion sketch design by interactive genetic algorithms

    Science.gov (United States)

    Mok, P. Y.; Wang, X. X.; Xu, J.; Kwok, Y. L.

    2012-11-01

    Computer aided design is vitally important for the modern industry, particularly for the creative industry. Fashion industry faced intensive challenges to shorten the product development process. In this paper, a methodology is proposed for sketch design based on interactive genetic algorithms. The sketch design system consists of a sketch design model, a database and a multi-stage sketch design engine. First, a sketch design model is developed based on the knowledge of fashion design to describe fashion product characteristics by using parameters. Second, a database is built based on the proposed sketch design model to define general style elements. Third, a multi-stage sketch design engine is used to construct the design. Moreover, an interactive genetic algorithm (IGA) is used to accelerate the sketch design process. The experimental results have demonstrated that the proposed method is effective in helping laypersons achieve satisfied fashion design sketches.

  7. From Genetics to Genetic Algorithms

    Indian Academy of Sciences (India)

    artificial genetic system) string feature or ... called the genotype whereas it is called a structure in artificial genetic ... assigned a fitness value based on the cost function. Better ..... way it has produced complex, intelligent living organisms capable of ...

  8. Anomalous Diffusion within the Transcriptome as a Bio-Inspired Computing Framework for Resilience

    Directory of Open Access Journals (Sweden)

    William Seffens

    2017-07-01

    Full Text Available Much of biology-inspired computer science is based on the Central Dogma, as implemented with genetic algorithms or evolutionary computation. That 60-year-old biological principle based on the genome, transcriptome and proteasome is becoming overshadowed by a new paradigm of complex ordered associations and connections between layers of biological entities, such as interactomes, metabolomics, etc. We define a new hierarchical concept as the “Connectosome”, and propose new venues of computational data structures based on a conceptual framework called “Grand Ensemble” which contains the Central Dogma as a subset. Connectedness and communication within and between living or biology-inspired systems comprise ensembles from which a physical computing system can be conceived. In this framework the delivery of messages is filtered by size and a simple and rapid semantic analysis of their content. This work aims to initiate discussion on the Grand Ensemble in network biology as a representation of a Persistent Turing Machine. This framework adding interaction and persistency to the classic Turing-machine model uses metrics based on resilience that has application to dynamic optimization problem solving in Genetic Programming.

  9. An Improved Hierarchical Genetic Algorithm for Sheet Cutting Scheduling with Process Constraints

    Directory of Open Access Journals (Sweden)

    Yunqing Rao

    2013-01-01

    Full Text Available For the first time, an improved hierarchical genetic algorithm for sheet cutting problem which involves n cutting patterns for m non-identical parallel machines with process constraints has been proposed in the integrated cutting stock model. The objective of the cutting scheduling problem is minimizing the weighted completed time. A mathematical model for this problem is presented, an improved hierarchical genetic algorithm (ant colony—hierarchical genetic algorithm is developed for better solution, and a hierarchical coding method is used based on the characteristics of the problem. Furthermore, to speed up convergence rates and resolve local convergence issues, a kind of adaptive crossover probability and mutation probability is used in this algorithm. The computational result and comparison prove that the presented approach is quite effective for the considered problem.

  10. An improved hierarchical genetic algorithm for sheet cutting scheduling with process constraints.

    Science.gov (United States)

    Rao, Yunqing; Qi, Dezhong; Li, Jinling

    2013-01-01

    For the first time, an improved hierarchical genetic algorithm for sheet cutting problem which involves n cutting patterns for m non-identical parallel machines with process constraints has been proposed in the integrated cutting stock model. The objective of the cutting scheduling problem is minimizing the weighted completed time. A mathematical model for this problem is presented, an improved hierarchical genetic algorithm (ant colony--hierarchical genetic algorithm) is developed for better solution, and a hierarchical coding method is used based on the characteristics of the problem. Furthermore, to speed up convergence rates and resolve local convergence issues, a kind of adaptive crossover probability and mutation probability is used in this algorithm. The computational result and comparison prove that the presented approach is quite effective for the considered problem.

  11. A Unifying Model for the Analysis of Phenotypic, Genetic and Geographic Data

    DEFF Research Database (Denmark)

    Guillot, Gilles; Rena, Sabrina; Ledevin, Ronan

    2012-01-01

    Recognition of evolutionary units (species, populations) requires integrating several kinds of data such as genetic or phenotypic markers or spatial information, in order to get a comprehensive view concerning the dierentiation of the units. We propose a statistical model with a double original...... advantage: (i) it incorporates information about the spatial distribution of the samples, with the aim to increase inference power and to relate more explicitly observed patterns to geography; and (ii) it allows one to analyze genetic and phenotypic data within a unied model and inference framework, thus...... an intricate case of inter- and intra-species dierentiation based on an original data-set of georeferenced genetic and morphometric markers obtained on Myodes voles from Sweden. A computer program is made available as an extension of the R package Geneland....

  12. An Analytic Solution to the Computation of Power and Sample Size for Genetic Association Studies under a Pleiotropic Mode of Inheritance.

    Science.gov (United States)

    Gordon, Derek; Londono, Douglas; Patel, Payal; Kim, Wonkuk; Finch, Stephen J; Heiman, Gary A

    2016-01-01

    Our motivation here is to calculate the power of 3 statistical tests used when there are genetic traits that operate under a pleiotropic mode of inheritance and when qualitative phenotypes are defined by use of thresholds for the multiple quantitative phenotypes. Specifically, we formulate a multivariate function that provides the probability that an individual has a vector of specific quantitative trait values conditional on having a risk locus genotype, and we apply thresholds to define qualitative phenotypes (affected, unaffected) and compute penetrances and conditional genotype frequencies based on the multivariate function. We extend the analytic power and minimum-sample-size-necessary (MSSN) formulas for 2 categorical data-based tests (genotype, linear trend test [LTT]) of genetic association to the pleiotropic model. We further compare the MSSN of the genotype test and the LTT with that of a multivariate ANOVA (Pillai). We approximate the MSSN for statistics by linear models using a factorial design and ANOVA. With ANOVA decomposition, we determine which factors most significantly change the power/MSSN for all statistics. Finally, we determine which test statistics have the smallest MSSN. In this work, MSSN calculations are for 2 traits (bivariate distributions) only (for illustrative purposes). We note that the calculations may be extended to address any number of traits. Our key findings are that the genotype test usually has lower MSSN requirements than the LTT. More inclusive thresholds (top/bottom 25% vs. top/bottom 10%) have higher sample size requirements. The Pillai test has a much larger MSSN than both the genotype test and the LTT, as a result of sample selection. With these formulas, researchers can specify how many subjects they must collect to localize genes for pleiotropic phenotypes. © 2017 S. Karger AG, Basel.

  13. Reverse genetics with animal viruses. NSV reverse genetics

    International Nuclear Information System (INIS)

    Mebatsion, T.

    2005-01-01

    New strategies to genetically manipulate the genomes of several important animal pathogens have been established in recent years. This article focuses on the reverse genetics techniques, which enables genetic manipulation of the genomes of non-segmented negative-sense RNA viruses. Recovery of a negative-sense RNA virus entirely from cDNA was first achieved for rabies virus in 1994. Since then, reverse genetic systems have been established for several pathogens of medical and veterinary importance. Based on the reverse genetics technique, it is now possible to design safe and more effective live attenuated vaccines against important viral agents. In addition, genetically tagged recombinant viruses can be designed to facilitate serological differentiation of vaccinated animals from infected animals. The approach of delivering protective immunogens of different pathogens using a single vector was made possible with the introduction of the reverse genetics system, and these novel broad-spectrum vaccine vectors have potential applications in improving animal health in developing countries. (author)

  14. Parameter Control of Genetic Algorithms by Learning and Simulation of Bayesian Networks——A Case Study for the Optimal Ordering of Tables

    Institute of Scientific and Technical Information of China (English)

    Concha Bielza; Juan A.Fernández del Pozo; Pedro Larra(n)aga

    2013-01-01

    Parameter setting for evolutionary algorithms is still an important issue in evolutionary computation.There are two main approaches to parameter setting:parameter tuning and parameter control.In this paper,we introduce self-adaptive parameter control of a genetic algorithm based on Bayesian network learning and simulation.The nodes of this Bayesian network are genetic algorithm parameters to be controlled.Its structure captures probabilistic conditional (in)dependence relationships between the parameters.They are learned from the best individuals,i.e.,the best configurations of the genetic algorithm.Individuals are evaluated by running the genetic algorithm for the respective parameter configuration.Since all these runs are time-consuming tasks,each genetic algorithm uses a small-sized population and is stopped before convergence.In this way promising individuals should not be lost.Experiments with an optimal search problem for simultaneous row and column orderings yield the same optima as state-of-the-art methods but with a sharp reduction in computational time.Moreover,our approach can cope with as yet unsolved high-dimensional problems.

  15. Cultural-Based Genetic Tabu Algorithm for Multiobjective Job Shop Scheduling

    Directory of Open Access Journals (Sweden)

    Yuzhen Yang

    2014-01-01

    Full Text Available The job shop scheduling problem, which has been dealt with by various traditional optimization methods over the decades, has proved to be an NP-hard problem and difficult in solving, especially in the multiobjective field. In this paper, we have proposed a novel quadspace cultural genetic tabu algorithm (QSCGTA to solve such problem. This algorithm provides a different structure from the original cultural algorithm in containing double brief spaces and population spaces. These spaces deal with different levels of populations globally and locally by applying genetic and tabu searches separately and exchange information regularly to make the process more effective towards promising areas, along with modified multiobjective domination and transform functions. Moreover, we have presented a bidirectional shifting for the decoding process of job shop scheduling. The computational results we presented significantly prove the effectiveness and efficiency of the cultural-based genetic tabu algorithm for the multiobjective job shop scheduling problem.

  16. Emerging experimental and computational technologies for purpose designed engineering of photosynthetic prokaryotes

    KAUST Repository

    Lindblad, Peter

    2016-01-25

    With recent advances in synthetic molecular tools to be used in photosynthetic prokaryotes, like cyanobacteria, it is possible to custom design and construct microbial cells for specific metabolic functions. This cross-disciplinary area of research has emerged within the interfaces of advanced genetic engineering, computational science, and molecular biotechnology. We have initiated the development of a genetic toolbox, using a synthetic biology approach, to custom design, engineer and construct cyanobacteria for selected function and metabolism. One major bottleneck is a controlled transcription and translation of introduced genetic constructs. An additional major issue is genetic stability. I will present and discuss recent progress in our development of genetic tools for advanced cyanobacterial biotechnology. Progress on understanding the electron pathways in native and engineered cyanobacterial enzymes and heterologous expression of non-native enymzes in cyanobacterial cells will be highlighted. Finally, I will discuss our attempts to merge synthetic biology with synthetic chemistry to explore fundamantal questions of protein design and function.

  17. A Genetic-Algorithms-Based Approach for Programming Linear and Quadratic Optimization Problems with Uncertainty

    Directory of Open Access Journals (Sweden)

    Weihua Jin

    2013-01-01

    Full Text Available This paper proposes a genetic-algorithms-based approach as an all-purpose problem-solving method for operation programming problems under uncertainty. The proposed method was applied for management of a municipal solid waste treatment system. Compared to the traditional interactive binary analysis, this approach has fewer limitations and is able to reduce the complexity in solving the inexact linear programming problems and inexact quadratic programming problems. The implementation of this approach was performed using the Genetic Algorithm Solver of MATLAB (trademark of MathWorks. The paper explains the genetic-algorithms-based method and presents details on the computation procedures for each type of inexact operation programming problems. A comparison of the results generated by the proposed method based on genetic algorithms with those produced by the traditional interactive binary analysis method is also presented.

  18. Genetic addiction: selfish gene's strategy for symbiosis in the genome.

    Science.gov (United States)

    Mochizuki, Atsushi; Yahara, Koji; Kobayashi, Ichizo; Iwasa, Yoh

    2006-02-01

    The evolution and maintenance of the phenomenon of postsegregational host killing or genetic addiction are paradoxical. In this phenomenon, a gene complex, once established in a genome, programs death of a host cell that has eliminated it. The intact form of the gene complex would survive in other members of the host population. It is controversial as to why these genetic elements are maintained, due to the lethal effects of host killing, or perhaps some other properties are beneficial to the host. We analyzed their population dynamics by analytical methods and computer simulations. Genetic addiction turned out to be advantageous to the gene complex in the presence of a competitor genetic element. The advantage is, however, limited in a population without spatial structure, such as that in a well-mixed liquid culture. In contrast, in a structured habitat, such as the surface of a solid medium, the addiction gene complex can increase in frequency, irrespective of its initial density. Our demonstration that genomes can evolve through acquisition of addiction genes has implications for the general question of how a genome can evolve as a community of potentially selfish genes.

  19. Measuring genetic knowledge: a brief survey instrument for adolescents and adults.

    Science.gov (United States)

    Fitzgerald-Butt, S M; Bodine, A; Fry, K M; Ash, J; Zaidi, A N; Garg, V; Gerhardt, C A; McBride, K L

    2016-02-01

    Basic knowledge of genetics is essential for understanding genetic testing and counseling. The lack of a written, English language, validated, published measure has limited our ability to evaluate genetic knowledge of patients and families. Here, we begin the psychometric analysis of a true/false genetic knowledge measure. The 18-item measure was completed by parents of children with congenital heart defects (CHD) (n = 465) and adolescents and young adults with CHD (age: 15-25, n = 196) with a mean total correct score of 12.6 [standard deviation (SD) = 3.5, range: 0-18]. Utilizing exploratory factor analysis, we determined that one to three correlated factors, or abilities, were captured by our measure. Through confirmatory factor analysis, we determined that the two factor model was the best fit. Although it was necessary to remove two items, the remaining items exhibited adequate psychometric properties in a multidimensional item response theory analysis. Scores for each factor were computed, and a sum-score conversion table was derived. We conclude that this genetic knowledge measure discriminates best at low knowledge levels and is therefore well suited to determine a minimum adequate amount of genetic knowledge. However, further reliability testing and validation in diverse research and clinical settings is needed. © 2015 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

  20. Raps markers for genetic diversity analysis in rice (Oryza sativa L)

    Energy Technology Data Exchange (ETDEWEB)

    Alvarez, A; Fuentes, Jorge L [Centro de Estudios Aplicados al Desarrollo Nuclear, La Habana (Cuba); Deus, Juan E [Instituto de Investigaciones del Arroz, Habana (Cuba); Duque, Maria C [Centro Internacional de la Agricultura Tropical. Proyecto de Arroz , Cali (Colombia)

    1999-07-01

    The establishment of relationships between genotypes existing in gene banks that may be used in new crosses, and about genetic diversity in available germplasm, is very useful for plant breeders. In this work, a genetic diversity analysis among 20 varieties of the Cuban rice germplasm bank was performed by using RAPD markers. Twenty four decamer primers were screened which produced 61 polymorphic bands out of 105 consistent and reproducible amplified fragments (58.1 %). The proportion of polymorphic bands varied for each primer, with an average of 3 polymorphic bands per primer, these results agreed with previous reports on RAPD polymorphism in rice germplasm. Depending on the primer, 1 to 7 distinct patterns were obtained among the screened genotypes. Pair-wise genetic distances between genotypes were computed based on Dice's coefficient. Three major, statistically robust groups were obtained in the UPGMA dendrogram (A, B and C) which clearly corresponded to different genetic pools. Additionally, more insight could be gained according to the sub-grouping pattern within group A, which included the principal semi-dwarf commercial varieties. The present study allowed to prove the efficiency of RAPD markers for genetic diversity analysis in closely related germplasm, particularly for the semi-dwarf Cuban commercial rice cultivars. Also, the existence of a narrow genetic base among these varieties has been confirmed, pointing at the urgent necessity of widen it.

  1. Raps markers for genetic diversity analysis in rice (Oryza sativa L)

    International Nuclear Information System (INIS)

    Alvarez, A.; Fuentes, Jorge L.; Deus, Juan E.; Duque, Maria C.

    1999-01-01

    The establishment of relationships between genotypes existing in gene banks that may be used in new crosses, and about genetic diversity in available germplasm, is very useful for plant breeders. In this work, a genetic diversity analysis among 20 varieties of the Cuban rice germplasm bank was performed by using RAPD markers. Twenty four decamer primers were screened which produced 61 polymorphic bands out of 105 consistent and reproducible amplified fragments (58.1 %). The proportion of polymorphic bands varied for each primer, with an average of 3 polymorphic bands per primer, these results agreed with previous reports on RAPD polymorphism in rice germplasm. Depending on the primer, 1 to 7 distinct patterns were obtained among the screened genotypes. Pair-wise genetic distances between genotypes were computed based on Dice's coefficient. Three major, statistically robust groups were obtained in the UPGMA dendrogram (A, B and C) which clearly corresponded to different genetic pools. Additionally, more insight could be gained according to the sub-grouping pattern within group A, which included the principal semi-dwarf commercial varieties. The present study allowed to prove the efficiency of RAPD markers for genetic diversity analysis in closely related germplasm, particularly for the semi-dwarf Cuban commercial rice cultivars. Also, the existence of a narrow genetic base among these varieties has been confirmed, pointing at the urgent necessity of widen it

  2. Genetic programs can be compressed and autonomously decompressed in live cells

    Science.gov (United States)

    Lapique, Nicolas; Benenson, Yaakov

    2018-04-01

    Fundamental computer science concepts have inspired novel information-processing molecular systems in test tubes1-13 and genetically encoded circuits in live cells14-21. Recent research has shown that digital information storage in DNA, implemented using deep sequencing and conventional software, can approach the maximum Shannon information capacity22 of two bits per nucleotide23. In nature, DNA is used to store genetic programs, but the information content of the encoding rarely approaches this maximum24. We hypothesize that the biological function of a genetic program can be preserved while reducing the length of its DNA encoding and increasing the information content per nucleotide. Here we support this hypothesis by describing an experimental procedure for compressing a genetic program and its subsequent autonomous decompression and execution in human cells. As a test-bed we choose an RNAi cell classifier circuit25 that comprises redundant DNA sequences and is therefore amenable for compression, as are many other complex gene circuits15,18,26-28. In one example, we implement a compressed encoding of a ten-gene four-input AND gate circuit using only four genetic constructs. The compression principles applied to gene circuits can enable fitting complex genetic programs into DNA delivery vehicles with limited cargo capacity, and storing compressed and biologically inert programs in vivo for on-demand activation.

  3. A Tomographic method based on genetic algorithms

    International Nuclear Information System (INIS)

    Turcanu, C.; Alecu, L.; Craciunescu, T.; Niculae, C.

    1997-01-01

    Computerized tomography being a non-destructive and non-evasive technique is frequently used in medical application to generate three dimensional images of objects. Genetic algorithms are efficient, domain independent for a large variety of problems. The proposed method produces good quality reconstructions even in case of very small number of projection angles. It requests no a priori knowledge about the solution and takes into account the statistical uncertainties. The main drawback of the method is the amount of computer memory and time needed. (author)

  4. Genetic search feature selection for affective modeling

    DEFF Research Database (Denmark)

    Martínez, Héctor P.; Yannakakis, Georgios N.

    2010-01-01

    Automatic feature selection is a critical step towards the generation of successful computational models of affect. This paper presents a genetic search-based feature selection method which is developed as a global-search algorithm for improving the accuracy of the affective models built....... The method is tested and compared against sequential forward feature selection and random search in a dataset derived from a game survey experiment which contains bimodal input features (physiological and gameplay) and expressed pairwise preferences of affect. Results suggest that the proposed method...

  5. DTREEv2, a computer-based support system for the risk assessment of genetically modified plants

    NARCIS (Netherlands)

    Pertry, I.; Nothegger, C.; Sweet, J.; Kuiper, H.A.; Davies, H.; Iserentant, D.; Hull, R.; Mezzetti, B.; Messens, K.; Loose, De M.; Oliveira, de D.; Burssens, S.; Gheysen, G.; Tzotzos, G.

    2014-01-01

    Risk assessment of genetically modified organisms (GMOs) remains a contentious area and a major factor influencing the adoption of agricultural biotech. Methodologically, in many countries, risk assessment is conducted by expert committees with little or no recourse to databases and expert systems

  6. Data mining in soft computing framework: a survey.

    Science.gov (United States)

    Mitra, S; Pal, S K; Mitra, P

    2002-01-01

    The present article provides a survey of the available literature on data mining using soft computing. A categorization has been provided based on the different soft computing tools and their hybridizations used, the data mining function implemented, and the preference criterion selected by the model. The utility of the different soft computing methodologies is highlighted. Generally fuzzy sets are suitable for handling the issues related to understandability of patterns, incomplete/noisy data, mixed media information and human interaction, and can provide approximate solutions faster. Neural networks are nonparametric, robust, and exhibit good learning and generalization capabilities in data-rich environments. Genetic algorithms provide efficient search algorithms to select a model, from mixed media data, based on some preference criterion/objective function. Rough sets are suitable for handling different types of uncertainty in data. Some challenges to data mining and the application of soft computing methodologies are indicated. An extensive bibliography is also included.

  7. A Simulation Study of Mutations in the Genetic Regulatory Hierarchy for Butterfly Eyespot Focus Determination

    OpenAIRE

    Marcus, Jeffrey M.; Evans, Travis M.

    2008-01-01

    The color patterns on the wings of butterflies have been an important model system in evolutionary developmental biology. A recent computational model tested genetic regulatory hierarchies hypothesized to underlie the formation of butterfly eyespot foci (Evans and Marcus, 2006). The computational model demonstrated that one proposed hierarchy was incapable of reproducing the known patterns of gene expression associated with eyespot focus determination in wild-type butterflies, but that two sl...

  8. Design of PID Controller Simulator based on Genetic Algorithm

    Directory of Open Access Journals (Sweden)

    Fahri VATANSEVER

    2013-08-01

    Full Text Available PID (Proportional Integral and Derivative controllers take an important place in the field of system controlling. Various methods such as Ziegler-Nichols, Cohen-Coon, Chien Hrones Reswick (CHR and Wang-Juang-Chan are available for the design of such controllers benefiting from the system time and frequency domain data. These controllers are in compliance with system properties under certain criteria suitable to the system. Genetic algorithms have become widely used in control system applications in parallel to the advances in the field of computer and artificial intelligence. In this study, PID controller designs have been carried out by means of classical methods and genetic algorithms and comparative results have been analyzed. For this purpose, a graphical user interface program which can be used for educational purpose has been developed. For the definite (entered transfer functions, the suitable P, PI and PID controller coefficients have calculated by both classical methods and genetic algorithms and many parameters and responses of the systems have been compared and presented numerically and graphically

  9. Pose estimation for augmented reality applications using genetic algorithm.

    Science.gov (United States)

    Yu, Ying Kin; Wong, Kin Hong; Chang, Michael Ming Yuen

    2005-12-01

    This paper describes a genetic algorithm that tackles the pose-estimation problem in computer vision. Our genetic algorithm can find the rotation and translation of an object accurately when the three-dimensional structure of the object is given. In our implementation, each chromosome encodes both the pose and the indexes to the selected point features of the object. Instead of only searching for the pose as in the existing work, our algorithm, at the same time, searches for a set containing the most reliable feature points in the process. This mismatch filtering strategy successfully makes the algorithm more robust under the presence of point mismatches and outliers in the images. Our algorithm has been tested with both synthetic and real data with good results. The accuracy of the recovered pose is compared to the existing algorithms. Our approach outperformed the Lowe's method and the other two genetic algorithms under the presence of point mismatches and outliers. In addition, it has been used to estimate the pose of a real object. It is shown that the proposed method is applicable to augmented reality applications.

  10. A genetic epidemiology approach to cyber-security.

    Science.gov (United States)

    Gil, Santiago; Kott, Alexander; Barabási, Albert-László

    2014-07-16

    While much attention has been paid to the vulnerability of computer networks to node and link failure, there is limited systematic understanding of the factors that determine the likelihood that a node (computer) is compromised. We therefore collect threat log data in a university network to study the patterns of threat activity for individual hosts. We relate this information to the properties of each host as observed through network-wide scans, establishing associations between the network services a host is running and the kinds of threats to which it is susceptible. We propose a methodology to associate services to threats inspired by the tools used in genetics to identify statistical associations between mutations and diseases. The proposed approach allows us to determine probabilities of infection directly from observation, offering an automated high-throughput strategy to develop comprehensive metrics for cyber-security.

  11. Experiencing the genetic body: parents' encounters with pediatric clinical genetics.

    Science.gov (United States)

    Raspberry, Kelly; Skinner, Debra

    2007-01-01

    Because of advancements in genetic research and technologies, the clinical practice of genetics is becoming a prevalent component of biomedicine. As the genetic basis for more and more diseases are found, it is possible that ways of experiencing health, illness, identity, kin relations, and the body are becoming geneticized, or understood within a genetic model of disease. Yet, other models and relations that go beyond genetic explanations also shape interpretations of health and disease. This article explores how one group of individuals for whom genetic disorder is highly relevant formulates their views of the body in light of genetic knowledge. Using data from an ethnographic study of 106 parents or potential parents of children with known or suspected genetic disorders who were referred to a pediatric genetic counseling and evaluation clinic in the southeastern United States, we find that these parents do, to some degree, perceive of their children's disorders in terms of a genetic body that encompasses two principal qualities: a sense of predetermined health and illness and an awareness of a profound historicity that reaches into the past and extends into the present and future. They experience this genetic body as both fixed and historical, but they also express ideas of a genetic body made less deterministic by their own efforts and future possibilities. This account of parents' experiences with genetics and clinical practice contributes to a growing body of work on the ways in which genetic information and technologies are transforming popular and medical notions of the body, and with it, health, illness, kinship relations, and personal and social identities.

  12. A Hybrid Genetic Programming Algorithm for Automated Design of Dispatching Rules.

    Science.gov (United States)

    Nguyen, Su; Mei, Yi; Xue, Bing; Zhang, Mengjie

    2018-06-04

    Designing effective dispatching rules for production systems is a difficult and timeconsuming task if it is done manually. In the last decade, the growth of computing power, advanced machine learning, and optimisation techniques has made the automated design of dispatching rules possible and automatically discovered rules are competitive or outperform existing rules developed by researchers. Genetic programming is one of the most popular approaches to discovering dispatching rules in the literature, especially for complex production systems. However, the large heuristic search space may restrict genetic programming from finding near optimal dispatching rules. This paper develops a new hybrid genetic programming algorithm for dynamic job shop scheduling based on a new representation, a new local search heuristic, and efficient fitness evaluators. Experiments show that the new method is effective regarding the quality of evolved rules. Moreover, evolved rules are also significantly smaller and contain more relevant attributes.

  13. Genetic fuzzy system modeling and simulation of vascular behaviour

    DEFF Research Database (Denmark)

    Tang, Jiaowei; Boonen, Harrie C.M.

    Background: The purpose of our project is to identify the rule sets and their interaction within the framework of cardiovascular function. By an iterative process of computational simulation and experimental work, we strive to mimic the physiological basis for cardiovascular adaptive changes in c...... the pressure change of different blood vessels. Conclusion: Genetic fuzzy system is one of potential modeling methods in modeling and simulation of vascular behavior.......Background: The purpose of our project is to identify the rule sets and their interaction within the framework of cardiovascular function. By an iterative process of computational simulation and experimental work, we strive to mimic the physiological basis for cardiovascular adaptive changes...... in cardiovascular disease and ultimately improve pharmacotherapy. For this purpose, novel computational approaches incorporating adaptive properties, auto-regulatory control and rule sets will be assessed, properties that are commonly lacking in deterministic models based on differential equations. We hypothesize...

  14. "What is this genetics, anyway?" Understandings of genetics, illness causality and inheritance among British Pakistani users of genetic services.

    Science.gov (United States)

    Shaw, Alison; Hurst, Jane A

    2008-08-01

    Misconceptions about basic genetic concepts and inheritance patterns may be widespread in the general population. This paper investigates understandings of genetics, illness causality and inheritance among British Pakistanis referred to a UK genetics clinic. During participant observation of genetics clinic consultations and semi-structured interviews in Urdu or English in respondents' homes, we identified an array of environmental, behavioral and spiritual understandings of the causes of medical and intellectual problems. Misconceptions about the location of genetic information in the body and of genetic mechanisms of inheritance were common, reflected the range of everyday theories observed for White British patients and included the belief that a child receives more genetic material from the father than the mother. Despite some participants' conversational use of genetic terminology, some patients had assimilated genetic information in ways that conflict with genetic theory with potentially serious clinical consequences. Additionally, skepticism of genetic theories of illness reflected a rejection of a dominant discourse of genetic risk that stigmatizes cousin marriages. Patients referred to genetics clinics may not easily surrender their lay or personal theories about the causes of their own or their child's condition and their understandings of genetic risk. Genetic counselors may need to identify, work with and at times challenge patients' understandings of illness causality and inheritance.

  15. Tag SNP selection via a genetic algorithm.

    Science.gov (United States)

    Mahdevar, Ghasem; Zahiri, Javad; Sadeghi, Mehdi; Nowzari-Dalini, Abbas; Ahrabian, Hayedeh

    2010-10-01

    Single Nucleotide Polymorphisms (SNPs) provide valuable information on human evolutionary history and may lead us to identify genetic variants responsible for human complex diseases. Unfortunately, molecular haplotyping methods are costly, laborious, and time consuming; therefore, algorithms for constructing full haplotype patterns from small available data through computational methods, Tag SNP selection problem, are convenient and attractive. This problem is proved to be an NP-hard problem, so heuristic methods may be useful. In this paper we present a heuristic method based on genetic algorithm to find reasonable solution within acceptable time. The algorithm was tested on a variety of simulated and experimental data. In comparison with the exact algorithm, based on brute force approach, results show that our method can obtain optimal solutions in almost all cases and runs much faster than exact algorithm when the number of SNP sites is large. Our software is available upon request to the corresponding author.

  16. CGPD: Cancer Genetics and Proteomics Database - A Dataset for Computational Analysis and Online Cancer Diagnostic Centre

    Directory of Open Access Journals (Sweden)

    Muhammad Rizwan Riaz

    2014-06-01

    Full Text Available Cancer Genetics and Proteomics Database (CGPD is a repository for genetics and proteomics data of those Homo sapiens genes which are involved in Cancer. These genes are categorized in the database on the basis of cancer type. 72 genes of 13 types of cancers are considered in this database yet. Primers, promoters and peptides of these genes are also made available. Primers provided for each gene, with their features and conditions given to facilitate the researchers, are useful in PCR amplification, especially in cloning experiments. CGPD also contains Online Cancer Diagnostic Center (OCDC. It also contains transcription and translation tools to assist research work in progressive manner. The database is publicly available at http://www.cgpd.comyr.com.

  17. Enhanced computational methods for quantifying the effect of geographic and environmental isolation on genetic differentiation

    NARCIS (Netherlands)

    Botta, Filippo; Eriksen, Casper; Fontaine, Michael Christophe; Guillot, Gilles

    2015-01-01

    In a recent paper, Bradburd et al. (2013) proposed a model to quantify the relative effect ofgeographic and environmental distance on genetic differentiation. Here, we enhance this method in several ways. 1. We modify the covariance model so as to fit better with mainstream geostatistical models and

  18. Subjective versus objective risk in genetic counseling for hereditary breast and/or ovarian cancers

    Directory of Open Access Journals (Sweden)

    Sperduti Isabella

    2009-12-01

    Full Text Available Abstract Background Despite the fact that genetic counseling in oncology provides information regarding objective risks, it can be found a contrast between the subjective and objective risk. The aims of this study were to evaluate the accuracy of the perceived risk compared to the objective risk estimated by the BRCApro computer model and to evaluate any associations between medical, demographic and psychological variables and the accuracy of risk perception. Methods 130 subjects were given medical-demographic file, Cancer and Genetic Risk Perception, Hospital Anxiety-Depression Scale. It was also computed an objective evaluation of the risk by the BRCApro model. Results The subjective risk was significantly higher than objective risk. The risk of tumour was overestimated by 56%, and the genetic risk by 67%. The subjects with less cancer affected relatives significantly overestimated their risk of being mutation carriers and made a more innacurate estimation than high risk subjects. Conclusion The description of this sample shows: general overestimation of the risk, inaccurate perception compared to BRCApro calculation and a more accurate estimation in those subjects with more cancer affected relatives (high risk subjects. No correlation was found between the levels of perception of risk and anxiety and depression. Based on our findings, it is worth pursuing improved communication strategies about the actual cancer and genetic risk, especially for subjects at "intermediate and slightly increased risk" of developing an hereditary breast and/or ovarian cancer or of being mutation carrier.

  19. Laboratory colonisation and genetic bottlenecks in the tsetse fly Glossina pallidipes.

    Directory of Open Access Journals (Sweden)

    Marc Ciosi

    2014-02-01

    Full Text Available The IAEA colony is the only one available for mass rearing of Glossina pallidipes, a vector of human and animal African trypanosomiasis in eastern Africa. This colony is the source for Sterile Insect Technique (SIT programs in East Africa. The source population of this colony is unclear and its genetic diversity has not previously been evaluated and compared to field populations.We examined the genetic variation within and between the IAEA colony and its potential source populations in north Zimbabwe and the Kenya/Uganda border at 9 microsatellites loci to retrace the demographic history of the IAEA colony. We performed classical population genetics analyses and also combined historical and genetic data in a quantitative analysis using Approximate Bayesian Computation (ABC. There is no evidence of introgression from the north Zimbabwean population into the IAEA colony. Moreover, the ABC analyses revealed that the foundation and establishment of the colony was associated with a genetic bottleneck that has resulted in a loss of 35.7% of alleles and 54% of expected heterozygosity compared to its source population. Also, we show that tsetse control carried out in the 1990's is likely reduced the effective population size of the Kenya/Uganda border population.All the analyses indicate that the area of origin of the IAEA colony is the Kenya/Uganda border and that a genetic bottleneck was associated with the foundation and establishment of the colony. Genetic diversity associated with traits that are important for SIT may potentially have been lost during this genetic bottleneck which could lead to a suboptimal competitiveness of the colony males in the field. The genetic diversity of the colony is lower than that of field populations and so, studies using colony flies should be interpreted with caution when drawing general conclusions about G. pallidipes biology.

  20. Life system modeling and intelligent computing. Pt. I. Proceedings

    Energy Technology Data Exchange (ETDEWEB)

    Li, Kang; Irwin, George W. (eds.) [Belfast Queen' s Univ. (United Kingdom). School of Electronics, Electrical Engineering and Computer Science; Fei, Minrui; Jia, Li [Shanghai Univ. (China). School of Mechatronical Engineering and Automation

    2010-07-01

    This book is part I of a two-volume work that contains the refereed proceedings of the International Conference on Life System Modeling and Simulation, LSMS 2010 and the International Conference on Intelligent Computing for Sustainable Energy and Environment, ICSEE 2010, held in Wuxi, China, in September 2010. The 194 revised full papers presented were carefully reviewed and selected from over 880 submissions and recommended for publication by Springer in two volumes of Lecture Notes in Computer Science (LNCS) and one volume of Lecture Notes in Bioinformatics (LNBI). This particular volume of Lecture Notes in Computer Science (LNCS) includes 55 papers covering 7 relevant topics. The 55 papers in this volume are organized in topical sections on intelligent modeling, monitoring, and control of complex nonlinear systems; autonomy-oriented computing and intelligent agents; advanced theory and methodology in fuzzy systems and soft computing; computational intelligence in utilization of clean and renewable energy resources; intelligent modeling, control and supervision for energy saving and pollution reduction; intelligent methods in developing vehicles, engines and equipments; computational methods and intelligence in modeling genetic and biochemical networks and regulation. (orig.)

  1. Splitting Strategy for Simulating Genetic Regulatory Networks

    Directory of Open Access Journals (Sweden)

    Xiong You

    2014-01-01

    Full Text Available The splitting approach is developed for the numerical simulation of genetic regulatory networks with a stable steady-state structure. The numerical results of the simulation of a one-gene network, a two-gene network, and a p53-mdm2 network show that the new splitting methods constructed in this paper are remarkably more effective and more suitable for long-term computation with large steps than the traditional general-purpose Runge-Kutta methods. The new methods have no restriction on the choice of stepsize due to their infinitely large stability regions.

  2. A Hybrid Computational Intelligence Approach Combining Genetic Programming And Heuristic Classification for Pap-Smear Diagnosis

    DEFF Research Database (Denmark)

    Tsakonas, Athanasios; Dounias, Georgios; Jantzen, Jan

    2001-01-01

    The paper suggests the combined use of different computational intelligence (CI) techniques in a hybrid scheme, as an effective approach to medical diagnosis. Getting to know the advantages and disadvantages of each computational intelligence technique in the recent years, the time has come...

  3. Studies on the value of incorporating the effect of dominance in genetic evaluations of dairy cattle, beef cattle and swine

    Directory of Open Access Journals (Sweden)

    Van Tassel CP.

    1998-01-01

    Full Text Available Nonadditive genetic effects are currently ignored in national genetic evaluations of farm animals because of ignorance of thelevel of dominance variance for traits of interest and the difficult computational problems involved. Potential gains fromincluding the effects of dominance in genetic evaluations include “purification” of additive values and availability ofpredictions of specific combining abilities for each pair of prospective parents. This study focused on making evaluation withdominance effects feasible computationally and on ascertaining benefits of such an evaluation for dairy cattle, beef cattle,and swine. Using iteration on data, computing costs for evaluation with dominance effects included costs could be less thantwice expensive as with only an additive model. With Method Â, variance components could be estimated for problemsinvolving up to 10 millions equations. Dominance effects accounted for up to 10% of phenotypic variance; estimates werelarger for growth traits. As a percentage of additive variance, the estimate of dominance variance reached 78% for 21-d litterweight of swine and 47% for post weaning weight of beef cattle. When dominance effects are ignored, additive evaluationsare “contaminated”; effects are greatest for evaluations of dams in a single large family. These changes in ranking wereimportant for dairy cattle, especially for dams of full-sibs, but were less important for swine. Specific combining abilitiescannot be included in sire evaluations and need to be computed separately for each set of parents. The predictions of specificcombining abilities could be used in computerized mating programs via the Internet. Gains from including the dominanceeffect in genetic evaluations would be moderate but would outweigh expenditures to produce those evaluations.

  4. Feline genetics: clinical applications and genetic testing.

    Science.gov (United States)

    Lyons, Leslie A

    2010-11-01

    DNA testing for domestic cat diseases and appearance traits is a rapidly growing asset for veterinary medicine. Approximately 33 genes contain 50 mutations that cause feline health problems or alterations in the cat's appearance. A variety of commercial laboratories can now perform cat genetic diagnostics, allowing both the veterinary clinician and the private owner to obtain DNA test results. DNA is easily obtained from a cat via a buccal swab with a standard cotton bud or cytological brush, allowing DNA samples to be easily sent to any laboratory in the world. The DNA test results identify carriers of the traits, predict the incidence of traits from breeding programs, and influence medical prognoses and treatments. An overall goal of identifying these genetic mutations is the correction of the defect via gene therapies and designer drug therapies. Thus, genetic testing is an effective preventative medicine and a potential ultimate cure. However, genetic diagnostic tests may still be novel for many veterinary practitioners and their application in the clinical setting needs to have the same scrutiny as any other diagnostic procedure. This article will review the genetic tests for the domestic cat, potential sources of error for genetic testing, and the pros and cons of DNA results in veterinary medicine. Highlighted are genetic tests specific to the individual cat, which are a part of the cat's internal genome. Copyright © 2010 Elsevier Inc. All rights reserved.

  5. A Novel Real-coded Quantum-inspired Genetic Algorithm and Its Application in Data Reconciliation

    Directory of Open Access Journals (Sweden)

    Gao Lin

    2012-06-01

    Full Text Available Traditional quantum-inspired genetic algorithm (QGA has drawbacks such as premature convergence, heavy computational cost, complicated coding and decoding process etc. In this paper, a novel real-coded quantum-inspired genetic algorithm is proposed based on interval division thinking. Detailed comparisons with some similar approaches for some standard benchmark functions test validity of the proposed algorithm. Besides, the proposed algorithm is used in two typical nonlinear data reconciliation problems (distilling process and extraction process and simulation results show its efficiency in nonlinear data reconciliation problems.

  6. Parallel genetic algorithms with migration for the hybrid flow shop scheduling problem

    Directory of Open Access Journals (Sweden)

    K. Belkadi

    2006-01-01

    Full Text Available This paper addresses scheduling problems in hybrid flow shop-like systems with a migration parallel genetic algorithm (PGA_MIG. This parallel genetic algorithm model allows genetic diversity by the application of selection and reproduction mechanisms nearer to nature. The space structure of the population is modified by dividing it into disjoined subpopulations. From time to time, individuals are exchanged between the different subpopulations (migration. Influence of parameters and dedicated strategies are studied. These parameters are the number of independent subpopulations, the interconnection topology between subpopulations, the choice/replacement strategy of the migrant individuals, and the migration frequency. A comparison between the sequential and parallel version of genetic algorithm (GA is provided. This comparison relates to the quality of the solution and the execution time of the two versions. The efficiency of the parallel model highly depends on the parameters and especially on the migration frequency. In the same way this parallel model gives a significant improvement of computational time if it is implemented on a parallel architecture which offers an acceptable number of processors (as many processors as subpopulations.

  7. Design of reproducible polarized and non-polarized edge filters using genetic algorithm

    International Nuclear Information System (INIS)

    Ejigu, Efrem Kebede; Lacquet, B M

    2010-01-01

    Recent advancement in optical fibre communications technology is partly due to the advancement of optical thin film technology. The advancement of optical thin film technology includes the development of new and existing optical filter design methods. The genetic algorithm is one of the new design methods that show promising results in designing a number of complicated design specifications. It is the finding of this study that the genetic algorithm design method, through its optimization capability, can give more reliable and reproducible designs of any specifications. The design method in this study optimizes the thickness of each layer to get to the best possible solution. Its capability and unavoidable limitations in designing polarized and non-polarized edge filters from absorptive and dispersive materials is well demonstrated. It is also demonstrated that polarized and non-polarized designs from the genetic algorithm are reproducible with great success. This research has accomplished the great task of formulating a computer program using the genetic algorithm in a Matlab environment for the design of a reproducible polarized and non-polarized filters of any sort from any kind of materials

  8. Integrating molecular QTL data into genome-wide genetic association analysis: Probabilistic assessment of enrichment and colocalization.

    Directory of Open Access Journals (Sweden)

    Xiaoquan Wen

    2017-03-01

    Full Text Available We propose a novel statistical framework for integrating the result from molecular quantitative trait loci (QTL mapping into genome-wide genetic association analysis of complex traits, with the primary objectives of quantitatively assessing the enrichment of the molecular QTLs in complex trait-associated genetic variants and the colocalizations of the two types of association signals. We introduce a natural Bayesian hierarchical model that treats the latent association status of molecular QTLs as SNP-level annotations for candidate SNPs of complex traits. We detail a computational procedure to seamlessly perform enrichment, fine-mapping and colocalization analyses, which is a distinct feature compared to the existing colocalization analysis procedures in the literature. The proposed approach is computationally efficient and requires only summary-level statistics. We evaluate and demonstrate the proposed computational approach through extensive simulation studies and analyses of blood lipid data and the whole blood eQTL data from the GTEx project. In addition, a useful utility from our proposed method enables the computation of expected colocalization signals using simple characteristics of the association data. Using this utility, we further illustrate the importance of enrichment analysis on the ability to discover colocalized signals and the potential limitations of currently available molecular QTL data. The software pipeline that implements the proposed computation procedures, enloc, is freely available at https://github.com/xqwen/integrative.

  9. [Current options of preimplantion genetic screening and preimplantation genetic diagnostics].

    Science.gov (United States)

    Šimečková, V

    The aim of this work is to summarize the current knowledge about preimplantation genetic screening and diagnostics. A review article. Department of Gynecology and Obstetrics, District Hospital Šternberk, IVF Clinic, Olomouc. Preimplantation genetic testing is a complex of genetic and molecular cytogenetic examinations, which can help to detect abnormalities in embryos before transfer into the uterus of the mother. These specialized examinations are based on the latest findings in genetics and assisted reproduction. The preimplantation genetic testing is necessarily associated with a method of in vitro fertilization. It is performed on isolated blastomeres on the third day of embryo cultivation. Nowadays, it is preferred trophectoderm examination of cells from the five-day blastocysts. Generally speaking, after preimplantation genetic testing, we can select only embryos without genetic load to transfer into uterus. Preimplantation genetic testing is an important part of treatment of infertility. Complex diagnostics and treatment of infertile couples are increasingly influenced by the development and use of advanced genomic technologies. Further development and application of these modern methods require close cooperation between the field of assisted reproduction and clinical genetics.

  10. Prediction of quantitative phenotypes based on genetic networks: a case study in yeast sporulation

    Directory of Open Access Journals (Sweden)

    Shen Li

    2010-09-01

    Full Text Available Abstract Background An exciting application of genetic network is to predict phenotypic consequences for environmental cues or genetic perturbations. However, de novo prediction for quantitative phenotypes based on network topology is always a challenging task. Results Using yeast sporulation as a model system, we have assembled a genetic network from literature and exploited Boolean network to predict sporulation efficiency change upon deleting individual genes. We observe that predictions based on the curated network correlate well with the experimentally measured values. In addition, computational analysis reveals the robustness and hysteresis of the yeast sporulation network and uncovers several patterns of sporulation efficiency change caused by double gene deletion. These discoveries may guide future investigation of underlying mechanisms. We have also shown that a hybridized genetic network reconstructed from both temporal microarray data and literature is able to achieve a satisfactory prediction accuracy of the same quantitative phenotypes. Conclusions This case study illustrates the value of predicting quantitative phenotypes based on genetic network and provides a generic approach.

  11. Genetic Mapping

    Science.gov (United States)

    ... greatly advanced genetics research. The improved quality of genetic data has reduced the time required to identify a ... cases, a matter of months or even weeks. Genetic mapping data generated by the HGP's laboratories is freely accessible ...

  12. Orbit computation of the TELECOM-2D satellite with a Genetic Algorithm

    Science.gov (United States)

    Deleflie, Florent; Coulot, David; Vienne, Alain; Decosta, Romain; Richard, Pascal; Lasri, Mohammed Amjad

    2014-07-01

    In order to test a preliminary orbit determination method, we fit an orbit of the geostationary satellite TELECOM-2D, as if we did not know any a priori information on its trajectory. The method is based on a genetic algorithm coupled to an analytical propagator of the trajectory, that is used over a couple of days, and that uses a whole set of altazimutal data that are acquired by the tracking network made up of the two TAROT telescopes. The adjusted orbit is then compared to a numerical reference. The method is described, and the results are analyzed, as a step towards an operational method of preliminary orbit determination for uncatalogued objects.

  13. An Intelligent Model for Pairs Trading Using Genetic Algorithms.

    Science.gov (United States)

    Huang, Chien-Feng; Hsu, Chi-Jen; Chen, Chi-Chung; Chang, Bao Rong; Li, Chen-An

    2015-01-01

    Pairs trading is an important and challenging research area in computational finance, in which pairs of stocks are bought and sold in pair combinations for arbitrage opportunities. Traditional methods that solve this set of problems mostly rely on statistical methods such as regression. In contrast to the statistical approaches, recent advances in computational intelligence (CI) are leading to promising opportunities for solving problems in the financial applications more effectively. In this paper, we present a novel methodology for pairs trading using genetic algorithms (GA). Our results showed that the GA-based models are able to significantly outperform the benchmark and our proposed method is capable of generating robust models to tackle the dynamic characteristics in the financial application studied. Based upon the promising results obtained, we expect this GA-based method to advance the research in computational intelligence for finance and provide an effective solution to pairs trading for investment in practice.

  14. Genetic Engineering and the Amelioration of Genetic Defect

    Science.gov (United States)

    Lederberg, Joshua

    1970-01-01

    Discusses the claims for a brave new world of genetic manipulation" and concludes that if we could agree upon applying genetic (or any other effective) remedies to global problems we probably would need no rescourse to them. Suggests that effective methods of preventing genetic disease are prevention of mutations and detection and…

  15. An Efficient Algorithm for Computing Attractors of Synchronous And Asynchronous Boolean Networks

    Science.gov (United States)

    Zheng, Desheng; Yang, Guowu; Li, Xiaoyu; Wang, Zhicai; Liu, Feng; He, Lei

    2013-01-01

    Biological networks, such as genetic regulatory networks, often contain positive and negative feedback loops that settle down to dynamically stable patterns. Identifying these patterns, the so-called attractors, can provide important insights for biologists to understand the molecular mechanisms underlying many coordinated cellular processes such as cellular division, differentiation, and homeostasis. Both synchronous and asynchronous Boolean networks have been used to simulate genetic regulatory networks and identify their attractors. The common methods of computing attractors are that start with a randomly selected initial state and finish with exhaustive search of the state space of a network. However, the time complexity of these methods grows exponentially with respect to the number and length of attractors. Here, we build two algorithms to achieve the computation of attractors in synchronous and asynchronous Boolean networks. For the synchronous scenario, combing with iterative methods and reduced order binary decision diagrams (ROBDD), we propose an improved algorithm to compute attractors. For another algorithm, the attractors of synchronous Boolean networks are utilized in asynchronous Boolean translation functions to derive attractors of asynchronous scenario. The proposed algorithms are implemented in a procedure called geneFAtt. Compared to existing tools such as genYsis, geneFAtt is significantly faster in computing attractors for empirical experimental systems. Availability The software package is available at https://sites.google.com/site/desheng619/download. PMID:23585840

  16. 50. Brazilian congress on genetics. 50 years developing genetics. Abstracts

    International Nuclear Information System (INIS)

    2004-01-01

    Use of radioisotopes and ionizing radiations in genetics is presented. Several aspects related to men, animals,plants and microorganisms are reported highlighting biological radiation effects, evolution, mutagenesis and genetic engineering. Genetic mapping, gene mutations, genetic diversity, DNA damages, plant cultivation and plant grow are studied as well

  17. Deep Learning for Population Genetic Inference.

    Science.gov (United States)

    Sheehan, Sara; Song, Yun S

    2016-03-01

    Given genomic variation data from multiple individuals, computing the likelihood of complex population genetic models is often infeasible. To circumvent this problem, we introduce a novel likelihood-free inference framework by applying deep learning, a powerful modern technique in machine learning. Deep learning makes use of multilayer neural networks to learn a feature-based function from the input (e.g., hundreds of correlated summary statistics of data) to the output (e.g., population genetic parameters of interest). We demonstrate that deep learning can be effectively employed for population genetic inference and learning informative features of data. As a concrete application, we focus on the challenging problem of jointly inferring natural selection and demography (in the form of a population size change history). Our method is able to separate the global nature of demography from the local nature of selection, without sequential steps for these two factors. Studying demography and selection jointly is motivated by Drosophila, where pervasive selection confounds demographic analysis. We apply our method to 197 African Drosophila melanogaster genomes from Zambia to infer both their overall demography, and regions of their genome under selection. We find many regions of the genome that have experienced hard sweeps, and fewer under selection on standing variation (soft sweep) or balancing selection. Interestingly, we find that soft sweeps and balancing selection occur more frequently closer to the centromere of each chromosome. In addition, our demographic inference suggests that previously estimated bottlenecks for African Drosophila melanogaster are too extreme.

  18. Significance testing in ridge regression for genetic data

    Directory of Open Access Journals (Sweden)

    De Iorio Maria

    2011-09-01

    Full Text Available Abstract Background Technological developments have increased the feasibility of large scale genetic association studies. Densely typed genetic markers are obtained using SNP arrays, next-generation sequencing technologies and imputation. However, SNPs typed using these methods can be highly correlated due to linkage disequilibrium among them, and standard multiple regression techniques fail with these data sets due to their high dimensionality and correlation structure. There has been increasing interest in using penalised regression in the analysis of high dimensional data. Ridge regression is one such penalised regression technique which does not perform variable selection, instead estimating a regression coefficient for each predictor variable. It is therefore desirable to obtain an estimate of the significance of each ridge regression coefficient. Results We develop and evaluate a test of significance for ridge regression coefficients. Using simulation studies, we demonstrate that the performance of the test is comparable to that of a permutation test, with the advantage of a much-reduced computational cost. We introduce the p-value trace, a plot of the negative logarithm of the p-values of ridge regression coefficients with increasing shrinkage parameter, which enables the visualisation of the change in p-value of the regression coefficients with increasing penalisation. We apply the proposed method to a lung cancer case-control data set from EPIC, the European Prospective Investigation into Cancer and Nutrition. Conclusions The proposed test is a useful alternative to a permutation test for the estimation of the significance of ridge regression coefficients, at a much-reduced computational cost. The p-value trace is an informative graphical tool for evaluating the results of a test of significance of ridge regression coefficients as the shrinkage parameter increases, and the proposed test makes its production computationally feasible.

  19. A Genetic Toolkit for Dissecting Dopamine Circuit Function in Drosophila

    Directory of Open Access Journals (Sweden)

    Tingting Xie

    2018-04-01

    Full Text Available Summary: The neuromodulator dopamine (DA plays a key role in motor control, motivated behaviors, and higher-order cognitive processes. Dissecting how these DA neural networks tune the activity of local neural circuits to regulate behavior requires tools for manipulating small groups of DA neurons. To address this need, we assembled a genetic toolkit that allows for an exquisite level of control over the DA neural network in Drosophila. To further refine targeting of specific DA neurons, we also created reagents that allow for the conversion of any existing GAL4 line into Split GAL4 or GAL80 lines. We demonstrated how this toolkit can be used with recently developed computational methods to rapidly generate additional reagents for manipulating small subsets or individual DA neurons. Finally, we used the toolkit to reveal a dynamic interaction between a small subset of DA neurons and rearing conditions in a social space behavioral assay. : The rapid analysis of how dopaminergic circuits regulate behavior is limited by the genetic tools available to target and manipulate small numbers of these neurons. Xie et al. present genetic tools in Drosophila that allow rational targeting of sparse dopaminergic neuronal subsets and selective knockdown of dopamine signaling. Keywords: dopamine, genetics, behavior, neural circuits, neuromodulation, Drosophila

  20. Genetics educational needs in China: physicians' experience and knowledge of genetic testing.

    Science.gov (United States)

    Li, Jing; Xu, Tengda; Yashar, Beverly M

    2015-09-01

    The aims of this study were to explore the relationship between physicians' knowledge and utilization of genetic testing and to explore genetics educational needs in China. An anonymous survey about experience, attitudes, and knowledge of genetic testing was conducted among physicians affiliated with Peking Union Medical College Hospital during their annual health evaluation. A personal genetics knowledge score was developed and predictors of personal genetics knowledge score were evaluated. Sixty-four physicians (33% male) completed the survey. Fifty-eight percent of them had used genetic testing in their clinical practice. Using a 4-point scale, mean knowledge scores of six common genetic testing techniques ranged from 1.7 ± 0.9 to 2.4 ± 1.0, and the average personal genetics knowledge score was 2.1 ± 0.8. In regression analysis, significant predictors of higher personal genetics knowledge score were ordering of genetic testing, utilization of pedigrees, higher medical degree, and recent genetics training (P education. This study demonstrated a sizable gap between Chinese physicians' knowledge and utilization of genetic testing. Participants had high self-perceived genetics educational needs. Development of genetics educational platforms is both warranted and desired in China.Genet Med 17 9, 757-760.

  1. Contemporary Genetics for Gender Researchers: Not Your Grandma's Genetics Anymore

    Science.gov (United States)

    Salk, Rachel H.; Hyde, Janet S.

    2012-01-01

    Over the past century, much of genetics was deterministic, and feminist researchers framed justified criticisms of genetics research. However, over the past two decades, genetics research has evolved remarkably and has moved far from earlier deterministic approaches. Our article provides a brief primer on modern genetics, emphasizing contemporary…

  2. Eddy current testing probe optimization using a parallel genetic algorithm

    Directory of Open Access Journals (Sweden)

    Dolapchiev Ivaylo

    2008-01-01

    Full Text Available This paper uses the developed parallel version of Michalewicz's Genocop III Genetic Algorithm (GA searching technique to optimize the coil geometry of an eddy current non-destructive testing probe (ECTP. The electromagnetic field is computed using FEMM 2D finite element code. The aim of this optimization was to determine coil dimensions and positions that improve ECTP sensitivity to physical properties of the tested devices.

  3. J. Genet. classic 101

    Indian Academy of Sciences (India)

    Journal of Genetics, Vol. 85, No. 2, August 2006. 101. Page 2. J. Genet. classic. 102. Journal of Genetics, Vol. 85, No. 2, August 2006. Page 3. J. Genet. classic. Journal of Genetics, Vol. 85, No. 2, August 2006. 103. Page 4. J. Genet. classic. 104. Journal of Genetics, Vol. 85, No. 2, August 2006. Page 5. J. Genet. classic.

  4. J. Genet. classic 37

    Indian Academy of Sciences (India)

    Unknown

    Journal of Genetics, Vol. 84, No. 1, April 2005. 37. Page 2. J. Genet. classic. Journal of Genetics, Vol. 84, No. 1, April 2005. 38. Page 3. J. Genet. classic. Journal of Genetics, Vol. 84, No. 1, April 2005. 39. Page 4. J. Genet. classic. Journal of Genetics, Vol. 84, No. 1, April 2005. 40. Page 5. J. Genet. classic. Journal of ...

  5. A Comparison of Telephone Genetic Counseling and In-Person Genetic Counseling from the Genetic Counselor's Perspective.

    Science.gov (United States)

    Burgess, Kelly R; Carmany, Erin P; Trepanier, Angela M

    2016-02-01

    Growing demand for and limited geographic access to genetic counseling services is increasing the need for alternative service delivery models (SDM) like telephone genetic counseling (TGC). Little research has been done on genetic counselors' perspectives of the practice of TGC. We created an anonymous online survey to assess whether telephone genetic counselors believed the tasks identified in the ABGC (American Board of Genetic Counseling) Practice Analysis were performed similarly or differently in TGC compared to in person genetic counseling (IPGC). If there were differences noted, we sought to determine the nature of the differences and if additional training might be needed to address them. Eighty eight genetic counselors with experience in TGC completed some or all of the survey. Respondents identified differences in 13 (14.8%) of the 88 tasks studied. The tasks identified as most different in TGC were: "establishing rapport through verbal and nonverbal interactions" (60.2%; 50/83 respondents identified the task as different), "recognizing factors affecting the counseling interaction" (47.8%; 32/67), "assessing client/family emotions, support, etc." (40.1%; 27/66) and "educating clients about basic genetic concepts" (35.6%; 26/73). A slight majority (53.8%; 35/65) felt additional training was needed to communicate information without visual aids and more effectively perform psychosocial assessments. In summary, although a majority of genetic counseling tasks are performed similarly between TGC and IPGC, TGC counselors recognize that specific training in the TGC model may be needed to address the key differences.

  6. SecureMA: protecting participant privacy in genetic association meta-analysis.

    Science.gov (United States)

    Xie, Wei; Kantarcioglu, Murat; Bush, William S; Crawford, Dana; Denny, Joshua C; Heatherly, Raymond; Malin, Bradley A

    2014-12-01

    Sharing genomic data is crucial to support scientific investigation such as genome-wide association studies. However, recent investigations suggest the privacy of the individual participants in these studies can be compromised, leading to serious concerns and consequences, such as overly restricted access to data. We introduce a novel cryptographic strategy to securely perform meta-analysis for genetic association studies in large consortia. Our methodology is useful for supporting joint studies among disparate data sites, where privacy or confidentiality is of concern. We validate our method using three multisite association studies. Our research shows that genetic associations can be analyzed efficiently and accurately across substudy sites, without leaking information on individual participants and site-level association summaries. Our software for secure meta-analysis of genetic association studies, SecureMA, is publicly available at http://github.com/XieConnect/SecureMA. Our customized secure computation framework is also publicly available at http://github.com/XieConnect/CircuitService. © The Author 2014. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

  7. Soft computing in green and renewable energy systems

    Energy Technology Data Exchange (ETDEWEB)

    Gopalakrishnan, Kasthurirangan [Iowa State Univ., Ames, IA (United States). Iowa Bioeconomy Inst.; US Department of Energy, Ames, IA (United States). Ames Lab; Kalogirou, Soteris [Cyprus Univ. of Technology, Limassol (Cyprus). Dept. of Mechanical Engineering and Materials Sciences and Engineering; Khaitan, Siddhartha Kumar (eds.) [Iowa State Univ. of Science and Technology, Ames, IA (United States). Dept. of Electrical Engineering and Computer Engineering

    2011-07-01

    Soft Computing in Green and Renewable Energy Systems provides a practical introduction to the application of soft computing techniques and hybrid intelligent systems for designing, modeling, characterizing, optimizing, forecasting, and performance prediction of green and renewable energy systems. Research is proceeding at jet speed on renewable energy (energy derived from natural resources such as sunlight, wind, tides, rain, geothermal heat, biomass, hydrogen, etc.) as policy makers, researchers, economists, and world agencies have joined forces in finding alternative sustainable energy solutions to current critical environmental, economic, and social issues. The innovative models, environmentally benign processes, data analytics, etc. employed in renewable energy systems are computationally-intensive, non-linear and complex as well as involve a high degree of uncertainty. Soft computing technologies, such as fuzzy sets and systems, neural science and systems, evolutionary algorithms and genetic programming, and machine learning, are ideal in handling the noise, imprecision, and uncertainty in the data, and yet achieve robust, low-cost solutions. As a result, intelligent and soft computing paradigms are finding increasing applications in the study of renewable energy systems. Researchers, practitioners, undergraduate and graduate students engaged in the study of renewable energy systems will find this book very useful. (orig.)

  8. Concomitant imaging and genetic findings in children with unilateral sensorineural hearing loss.

    Science.gov (United States)

    Gruber, M; Brown, C; Mahadevan, M; Neeff, M

    2017-08-01

    To describe the concomitant imaging and genetic findings in children diagnosed with non-syndromic unilateral sensorineural hearing loss. A retrospective cohort study was conducted of 60 children diagnosed between January 2005 and December 2015 in a tertiary-level paediatric institution. Average age at diagnosis was 4.3 years. All children were considered non-syndromic. Hearing loss was categorised as mild (17 children), moderate (17 children), severe (7 children) or profound (19 children). Imaging was performed in 43 children (71.66 per cent). Nineteen patients (44.2 per cent) had positive computed tomography or magnetic resonance imaging findings. Genetic testing was performed in 51 children (85 per cent). Sixteen children (31 per cent) tested positive to connexin 26 (GJB2); 1 patient (2 per cent) had a homozygous mutation of GJB2 and 15 were heterozygous carriers. Amongst children who tested positive as heterozygous carriers of a GJB2 mutation, there was a high rate of positive imaging findings (47 per cent compared to 37.2 per cent in the total cohort). A genetic abnormality was confirmed in 50 per cent of children with positive imaging findings who underwent genetic testing. Rates of concomitant imaging and genetic findings suggest that both investigations are of value in the study of these patients.

  9. Current Trend Towards Using Soft Computing Approaches to Phase Synchronization in Communication Systems

    Science.gov (United States)

    Drake, Jeffrey T.; Prasad, Nadipuram R.

    1999-01-01

    This paper surveys recent advances in communications that utilize soft computing approaches to phase synchronization. Soft computing, as opposed to hard computing, is a collection of complementary methodologies that act in producing the most desirable control, decision, or estimation strategies. Recently, the communications area has explored the use of the principal constituents of soft computing, namely, fuzzy logic, neural networks, and genetic algorithms, for modeling, control, and most recently for the estimation of phase in phase-coherent communications. If the receiver in a digital communications system is phase-coherent, as is often the case, phase synchronization is required. Synchronization thus requires estimation and/or control at the receiver of an unknown or random phase offset.

  10. Intrapair Similarity of Computer Self-Efficacy in Turkish Adolescent Twins

    Science.gov (United States)

    Deryakulu, Deniz; Mcilroy, David; Ursavas, Ömer Faruk; Çaliskan, Erkan

    2016-01-01

    The purpose of this study is to investigate genetic and environmental influences on computer self-efficacy. A total of 165 Turkish twin-pairs participated in the study. Participants' mean age was 12.45 (SD = 1.82). The results of paired t-test comparisons showed no significant differences in monozygotic, and both same-sex and opposite-sex…

  11. Genetic information, non-discrimination, and privacy protections in genetic counseling practice.

    Science.gov (United States)

    Prince, Anya E R; Roche, Myra I

    2014-12-01

    The passage of the Genetic Information Non Discrimination Act (GINA) was hailed as a pivotal achievement that was expected to calm the fears of both patients and research participants about the potential misuse of genetic information. However, 6 years later, patient and provider awareness of legal protections at both the federal and state level remains discouragingly low, thereby, limiting their potential effectiveness. The increasing demand for genetic testing will expand the number of individuals and families who could benefit from obtaining accurate information about the privacy and anti-discriminatory protections that GINA and other laws extend. In this paper we describe legal protections that are applicable to individuals seeking genetic counseling, review the literature on patient and provider fears of genetic discrimination and examine their awareness and understandings of existing laws, and summarize how genetic counselors currently discuss genetic discrimination. We then present three genetic counseling cases to illustrate issues of genetic discrimination and provide relevant information on applicable legal protections. Genetic counselors have an unprecedented opportunity, as well as the professional responsibility, to disseminate accurate knowledge about existing legal protections to their patients. They can strengthen their effectiveness in this role by achieving a greater knowledge of current protections including being able to identify specific steps that can help protect genetic information.

  12. A novel pseudoderivative-based mutation operator for real-coded adaptive genetic algorithms [v2; ref status: indexed, http://f1000r.es/1td

    OpenAIRE

    Maxinder S Kanwal; Avinash S Ramesh; Lauren A Huang

    2013-01-01

    Recent development of large databases, especially those in genetics and proteomics, is pushing the development of novel computational algorithms that implement rapid and accurate search strategies. One successful approach has been to use artificial intelligence and methods, including pattern recognition (e.g. neural networks) and optimization techniques (e.g. genetic algorithms). The focus of this paper is on optimizing the design of genetic algorithms by using an adaptive mutation rate that ...

  13. A CAL Program to Teach the Basic Principles of Genetic Engineering--A Change from the Traditional Approach.

    Science.gov (United States)

    Dewhurst, D. G.; And Others

    1989-01-01

    An interactive computer-assisted learning program written for the BBC microcomputer to teach the basic principles of genetic engineering is described. Discussed are the hardware requirements software, use of the program, and assessment. (Author/CW)

  14. Exploring Relationships Among Belief in Genetic Determinism, Genetics Knowledge, and Social Factors

    Science.gov (United States)

    Gericke, Niklas; Carver, Rebecca; Castéra, Jérémy; Evangelista, Neima Alice Menezes; Marre, Claire Coiffard; El-Hani, Charbel N.

    2017-12-01

    Genetic determinism can be described as the attribution of the formation of traits to genes, where genes are ascribed more causal power than what scientific consensus suggests. Belief in genetic determinism is an educational problem because it contradicts scientific knowledge, and is a societal problem because it has the potential to foster intolerant attitudes such as racism and prejudice against sexual orientation. In this article, we begin by investigating the very nature of belief in genetic determinism. Then, we investigate whether knowledge of genetics and genomics is associated with beliefs in genetic determinism. Finally, we explore the extent to which social factors such as gender, education, and religiosity are associated with genetic determinism. Methodologically, we gathered and analyzed data on beliefs in genetic determinism, knowledge of genetics and genomics, and social variables using the "Public Understanding and Attitudes towards Genetics and Genomics" (PUGGS) instrument. Our analyses of PUGGS responses from a sample of Brazilian university freshmen undergraduates indicated that (1) belief in genetic determinism was best characterized as a construct built up by two dimensions or belief systems: beliefs concerning social traits and beliefs concerning biological traits; (2) levels of belief in genetic determination of social traits were low, which contradicts prior work; (3) associations between knowledge of genetics and genomics and levels of belief in genetic determinism were low; and (4) social factors such as age and religiosity had stronger associations with beliefs in genetic determinism than knowledge. Although our study design precludes causal inferences, our results raise questions about whether enhancing genetic literacy will decrease or prevent beliefs in genetic determinism.

  15. Rare genetic diseases: update on diagnosis, treatment and online resources.

    Science.gov (United States)

    Pogue, Robert E; Cavalcanti, Denise P; Shanker, Shreya; Andrade, Rosangela V; Aguiar, Lana R; de Carvalho, Juliana L; Costa, Fabrício F

    2018-01-01

    Rare genetic diseases collectively impact a significant portion of the world's population. For many diseases there is limited information available, and clinicians can find difficulty in differentiating between clinically similar conditions. This leads to problems in genetic counseling and patient treatment. The biomedical market is affected because pharmaceutical and biotechnology industries do not see advantages in addressing rare disease treatments, or because the cost of the treatments is too high. By contrast, technological advances including DNA sequencing and analysis, together with computer-aided tools and online resources, are allowing a more thorough understanding of rare disorders. Here, we discuss how the collection of various types of information together with the use of new technologies is facilitating diagnosis and, consequently, treatment of rare diseases. Copyright © 2017 Elsevier Ltd. All rights reserved.

  16. GENIE: a software package for gene-gene interaction analysis in genetic association studies using multiple GPU or CPU cores

    Directory of Open Access Journals (Sweden)

    Wang Kai

    2011-05-01

    Full Text Available Abstract Background Gene-gene interaction in genetic association studies is computationally intensive when a large number of SNPs are involved. Most of the latest Central Processing Units (CPUs have multiple cores, whereas Graphics Processing Units (GPUs also have hundreds of cores and have been recently used to implement faster scientific software. However, currently there are no genetic analysis software packages that allow users to fully utilize the computing power of these multi-core devices for genetic interaction analysis for binary traits. Findings Here we present a novel software package GENIE, which utilizes the power of multiple GPU or CPU processor cores to parallelize the interaction analysis. GENIE reads an entire genetic association study dataset into memory and partitions the dataset into fragments with non-overlapping sets of SNPs. For each fragment, GENIE analyzes: 1 the interaction of SNPs within it in parallel, and 2 the interaction between the SNPs of the current fragment and other fragments in parallel. We tested GENIE on a large-scale candidate gene study on high-density lipoprotein cholesterol. Using an NVIDIA Tesla C1060 graphics card, the GPU mode of GENIE achieves a speedup of 27 times over its single-core CPU mode run. Conclusions GENIE is open-source, economical, user-friendly, and scalable. Since the computing power and memory capacity of graphics cards are increasing rapidly while their cost is going down, we anticipate that GENIE will achieve greater speedups with faster GPU cards. Documentation, source code, and precompiled binaries can be downloaded from http://www.cceb.upenn.edu/~mli/software/GENIE/.

  17. Mathematical programming models for solving in equal-sized facilities layout problems. A genetic search method

    International Nuclear Information System (INIS)

    Tavakkoli-Moghaddam, R.

    1999-01-01

    This paper present unequal-sized facilities layout solutions generated by a genetic search program. named Layout Design using a Genetic Algorithm) 9. The generalized quadratic assignment problem requiring pre-determined distance and material flow matrices as the input data and the continuous plane model employing a dynamic distance measure and a material flow matrix are discussed. Computational results on test problems are reported as compared with layout solutions generated by the branch - and bound algorithm a hybrid method merging simulated annealing and local search techniques, and an optimization process of an enveloped block

  18. Genetic polymorphisms of nine X-STR loci in four population groups from Inner Mongolia, China.

    Science.gov (United States)

    Hou, Qiao-Fang; Yu, Bin; Li, Sheng-Bin

    2007-02-01

    Nine short tandem repeat (STR) markers on the X chromosome (DXS101, DXS6789, DXS6799, DXS6804, DXS7132, DXS7133, DXS7423, DXS8378, and HPRTB) were analyzed in four population groups (Mongol, Ewenki, Oroqen, and Daur) from Inner Mongolia, China, in order to learn about the genetic diversity, forensic suitability, and possible genetic affinities of the populations. Frequency estimates, Hardy-Weinberg equilibrium, and other parameters of forensic interest were computed. The results revealed that the nine markers have a moderate degree of variability in the population groups. Most heterozygosity values for the nine loci range from 0.480 to 0.891, and there are evident differences of genetic variability among the populations. A UPGMA tree constructed on the basis of the generated data shows very low genetic distance between Mongol and Han (Xi'an) populations. Our results based on genetic distance analysis are consistent with the results of earlier studies based on linguistics and the immigration history and origin of these populations. The minisatellite loci on the X chromosome studied here are not only useful in showing significant genetic variation between the populations, but also are suitable for human identity testing among Inner Mongolian populations.

  19. J. Genet. classic 235

    Indian Academy of Sciences (India)

    Unknown

    Journal of Genetics, Vol. 83, No. 3, December 2004. 235. Page 2. J. Genet. classic. Journal of Genetics, Vol. 83, No. 3, December 2004. 236. Page 3. J. Genet. classic. Journal of Genetics, Vol. 83, No. 3, December 2004. 237. Page 4. J. Genet. classic. Journal of Genetics, Vol. 83, No. 3, December 2004. 238. Page 5 ...

  20. Genetic counseling and the ethical issues around direct to consumer genetic testing.

    Science.gov (United States)

    Hawkins, Alice K; Ho, Anita

    2012-06-01

    Over the last several years, direct to consumer(DTC) genetic testing has received increasing attention in the public, healthcare and academic realms. DTC genetic testing companies face considerable criticism and scepticism,particularly from the medical and genetic counseling community. This raises the question of what specific aspects of DTC genetic testing provoke concerns, and conversely,promises, for genetic counselors. This paper addresses this question by exploring DTC genetic testing through an ethic allens. By considering the fundamental ethical approaches influencing genetic counseling (the ethic of care and principle-based ethics) we highlight the specific ethical concerns raised by DTC genetic testing companies. Ultimately,when considering the ethics of DTC testing in a genetic counseling context, we should think of it as a balancing act. We need careful and detailed consideration of the risks and troubling aspects of such testing, as well as the potentially beneficial direct and indirect impacts of the increased availability of DTC genetic testing. As a result it is essential that genetic counselors stay informed and involved in the ongoing debate about DTC genetic testing and DTC companies. Doing so will ensure that the ethical theories and principles fundamental to the profession of genetic counseling are promoted not just in traditional counseling sessions,but also on a broader level. Ultimately this will help ensure that the public enjoys the benefits of an increasingly genetic based healthcare system.

  1. Towards an integrated ecosystem of R packages for the analysis of population genetic data

    Science.gov (United States)

    Computer programs have early on taken a central place in the analysis of population genetic data. The continuous increase in amount and diversity of data, together with the emergence of new pressing questions related to the adaptation of populations of plants and animals in the face of global change...

  2. A new hybrid genetic algorithm for optimizing the single and multivariate objective functions

    Energy Technology Data Exchange (ETDEWEB)

    Tumuluru, Jaya Shankar [Idaho National Laboratory; McCulloch, Richard Chet James [Idaho National Laboratory

    2015-07-01

    In this work a new hybrid genetic algorithm was developed which combines a rudimentary adaptive steepest ascent hill climbing algorithm with a sophisticated evolutionary algorithm in order to optimize complex multivariate design problems. By combining a highly stochastic algorithm (evolutionary) with a simple deterministic optimization algorithm (adaptive steepest ascent) computational resources are conserved and the solution converges rapidly when compared to either algorithm alone. In genetic algorithms natural selection is mimicked by random events such as breeding and mutation. In the adaptive steepest ascent algorithm each variable is perturbed by a small amount and the variable that caused the most improvement is incremented by a small step. If the direction of most benefit is exactly opposite of the previous direction with the most benefit then the step size is reduced by a factor of 2, thus the step size adapts to the terrain. A graphical user interface was created in MATLAB to provide an interface between the hybrid genetic algorithm and the user. Additional features such as bounding the solution space and weighting the objective functions individually are also built into the interface. The algorithm developed was tested to optimize the functions developed for a wood pelleting process. Using process variables (such as feedstock moisture content, die speed, and preheating temperature) pellet properties were appropriately optimized. Specifically, variables were found which maximized unit density, bulk density, tapped density, and durability while minimizing pellet moisture content and specific energy consumption. The time and computational resources required for the optimization were dramatically decreased using the hybrid genetic algorithm when compared to MATLAB's native evolutionary optimization tool.

  3. Employability of genetic counselors with a PhD in genetic counseling.

    Science.gov (United States)

    Wallace, Jody P; Myers, Melanie F; Huether, Carl A; Bedard, Angela C; Warren, Nancy Steinberg

    2008-06-01

    The development of a PhD in genetic counseling has been discussed for more than 20 years, yet the perspectives of employers have not been assessed. The goal of this qualitative study was to gain an understanding of the employability of genetic counselors with a PhD in genetic counseling by conducting interviews with United States employers of genetic counselors. Study participants were categorized according to one of the following practice areas: academic, clinical, government, industry, laboratory, or research. All participants were responsible for hiring genetic counselors in their institutions. Of the 30 employers interviewed, 23 envisioned opportunities for individuals with a PhD degree in genetic counseling, particularly in academic and research settings. Performing research and having the ability to be a principal investigator on a grant was the primary role envisioned for these individuals by 22/30 participants. Employers expect individuals with a PhD in genetic counseling to perform different roles than MS genetic counselors with a master's degree. This study suggests there is an employment niche for individuals who have a PhD in genetic counseling that complements, and does not compete with, master's prepared genetic counselors.

  4. [The role of the genetics history in genetics teaching].

    Science.gov (United States)

    Li, Ming-Hui

    2006-08-01

    The research of the scientific history and development status reflect the science and technology level of a nation. The genetic history is one of the branches of the life science and the 21st century is life science century. The genetics history in the teaching of genetics not only can help students get familiar with the birth and development of genetics, but also enhance their thinking ability and scientific qualities. The roles and approaches of teaching are discussed in this paper.

  5. Multimedia messages in genetics: design, development, and evaluation of a computer-based instructional resource for secondary school students in a Tay Sachs disease carrier screening program.

    Science.gov (United States)

    Gason, Alexandra A; Aitken, MaryAnne; Delatycki, Martin B; Sheffield, Edith; Metcalfe, Sylvia A

    2004-01-01

    Tay Sachs disease is a recessively inherited neurodegenerative disorder, for which carrier screening programs exist worldwide. Education for those offered a screening test is essential in facilitating informed decision-making. In Melbourne, Australia, we have designed, developed, and evaluated a computer-based instructional resource for use in the Tay Sachs disease carrier screening program for secondary school students attending Jewish schools. The resource entitled "Genetics in the Community: Tay Sachs disease" was designed on a platform of educational learning theory. The development of the resource included formative evaluation using qualitative data analysis supported by descriptive quantitative data. The final resource was evaluated within the screening program and compared with the standard oral presentation using a questionnaire. Knowledge outcomes were measured both before and after either of the educational formats. Data from the formative evaluation were used to refine the content and functionality of the final resource. The questionnaire evaluation of 302 students over two years showed the multimedia resource to be equally effective as an oral educational presentation in facilitating participants' knowledge construction. The resource offers a large number of potential benefits, which are not limited to the Tay Sachs disease carrier screening program setting, such as delivery of a consistent educational message, short delivery time, and minimum financial and resource commitment. This article outlines the value of considering educational theory and describes the process of multimedia development providing a framework that may be of value when designing genetics multimedia resources in general.

  6. Cumulative genetic damage in children exposed to preconception and intrauterine radiation

    International Nuclear Information System (INIS)

    Bross, I.D.; Natarajan, N.

    1980-01-01

    Using a mathematical model and newly developed computer software, the data from the Tri-State Leukemia Survey involving different combinations of radiation exposures to the father and mother prior to conception and to the mother during pregnancy were analyzed. The hypothesis that radiation exposure produces genetic damage which may be expressed in the child both as indicator disease and as leukemia was tested. The genetic damage was estimated in terms of the proportion affected by a given exposure. The relative risk of leukemia and certain other indicator diseases among those affected could then be estimated. The results show that there are at least two distinguishable risk groups, one group with lower (one or two exposures); and the other group with higher (two or three) radiation exposures

  7. Genetic secrets: Protecting privacy and confidentiality in the genetic era

    Energy Technology Data Exchange (ETDEWEB)

    Rothstein, M.A. [ed.

    1998-07-01

    Few developments are likely to affect human beings more profoundly in the long run than the discoveries resulting from advances in modern genetics. Although the developments in genetic technology promise to provide many additional benefits, their application to genetic screening poses ethical, social, and legal questions, many of which are rooted in issues of privacy and confidentiality. The ethical, practical, and legal ramifications of these and related questions are explored in depth. The broad range of topics includes: the privacy and confidentiality of genetic information; the challenges to privacy and confidentiality that may be projected to result from the emerging genetic technologies; the role of informed consent in protecting the confidentiality of genetic information in the clinical setting; the potential uses of genetic information by third parties; the implications of changes in the health care delivery system for privacy and confidentiality; relevant national and international developments in public policies, professional standards, and laws; recommendations; and the identification of research needs.

  8. Implementation of Genetic Algorithm in Control Structure of Induction Motor A.C. Drive

    Directory of Open Access Journals (Sweden)

    BRANDSTETTER, P.

    2014-11-01

    Full Text Available Modern concepts of control systems with digital signal processors allow the implementation of time-consuming control algorithms in real-time, for example soft computing methods. The paper deals with the design and technical implementation of a genetic algorithm for setting proportional and integral gain of the speed controller of the A.C. drive with the vector-controlled induction motor. Important simulations and experimental measurements have been realized that confirm the correctness of the proposed speed controller tuned by the genetic algorithm and the quality speed response of the A.C. drive with changing parameters and disturbance variables, such as changes in load torque.

  9. Genetic Algorithms for Case Adaptation

    International Nuclear Information System (INIS)

    Salem, A.M.; Mohamed, A.H.

    2008-01-01

    Case adaptation is the core of case based reasoning (CBR) approach that can modify the past solutions to solve new problems. It generally relies on the knowledge base and heuristics in order to achieve the required changes. It has always been a difficult process to designers within (CBR) cycle. Its difficulties can be referred to the large effort, and computational analysis needed for acquiring the knowledge's domain. To solve these problems, this research explores a new method that applying a genetic algorithm (GA) to CBR adaptation. However, it can decrease the computational complexity of determining the required changes of the problems especially those having a great amount of domain knowledge. besides, it can decrease the required time by dividing the design task into sub tasks those can be solved at the same time. Therefore, the proposed system can he practically applied for solving the complex problems. It can be used to perform a variety of design tasks on a broad set of application domains. However, it has been implemented for the tablet formulation as a domain of application. Proposed system has improved the accuracy performance of the CBR design systems

  10. Genetics in the art and art in genetics.

    Science.gov (United States)

    Bukvic, Nenad; Elling, John W

    2015-01-15

    "Healing is best accomplished when art and science are conjoined, when body and spirit are probed together", says Bernard Lown, in his book "The Lost Art of Healing". Art has long been a witness to disease either through diseases which affected artists or diseases afflicting objects of their art. In particular, artists have often portrayed genetic disorders and malformations in their work. Sometimes genetic disorders have mystical significance; other times simply have intrinsic interest. Recognizing genetic disorders is also an art form. From the very beginning of my work as a Medical Geneticist I have composed personal "algorithms" to piece together evidence of genetics syndromes and diseases from the observable signs and symptoms. In this paper we apply some 'gestalt' Genetic Syndrome Diagnostic algorithms to virtual patients found in some art masterpieces. In some the diagnosis is clear and in others the artists' depiction only supports a speculative differential diagnosis. Copyright © 2014 Elsevier B.V. All rights reserved.

  11. Genetic association of glutathione peroxidase-1 with coronary artery calcification in type 2 diabetes: a case control study with multi-slice computed tomography

    Directory of Open Access Journals (Sweden)

    Fujimoto Kei

    2007-09-01

    Full Text Available Abstract Background Although oxidative stress by accumulation of reactive oxygen species (ROS in diabetes has become evident, it remains unclear what genes, involved in redox balance, would determine susceptibility for development of atherosclerosis in diabetes. This study evaluated the effect of genetic polymorphism of enzymes producing or responsible for reducing ROS on coronary artery calcification in type 2 diabetes (T2D. Methods An index for coronary-arteriosclerosis, coronary artery calcium score (CACS was evaluated in 91 T2D patients using a multi-slice computed tomography. Patients were genotyped for ROS-scavenging enzymes, Glutathione peroxidase-1 (GPx-1, Catalase, Mn-SOD, Cu/Zn-SOD, as well as SNPs of NADPH oxidase as ROS-promoting elements, genes related to onset of T2D (CAPN10, ADRB3, PPAR gamma, FATP4. Age, blood pressure, BMI, HbA1c, lipid and duration of diabetes were evaluated for a multivariate regression analysis. Results CACS with Pro/Leu genotype of the GPx-1 gene was significantly higher than in those with Pro/Pro (744 ± 1,291 vs. 245 ± 399, respectively, p = 0.006. In addition, genotype frequency of Pro/Leu in those with CACS ≥ 1000 was significantly higher than in those with CACS OR = 3.61, CI = 0.97–13.42; p = 0.045 when tested for deviation from Hardy-Weinberg's equilibrium. Multivariate regression analyses revealed that CACS significantly correlated with GPx-1 genotypes and age. Conclusion The presence of Pro197Leu substitution of the GPx-1 gene may play a crucial role in determining genetic susceptibility to coronary-arteriosclerosis in T2D. The mechanism may be associated with a decreased ability to scavenge ROS with the variant GPx-1.

  12. Analysis of conditional genetic effects and variance components in developmental genetics.

    Science.gov (United States)

    Zhu, J

    1995-12-01

    A genetic model with additive-dominance effects and genotype x environment interactions is presented for quantitative traits with time-dependent measures. The genetic model for phenotypic means at time t conditional on phenotypic means measured at previous time (t-1) is defined. Statistical methods are proposed for analyzing conditional genetic effects and conditional genetic variance components. Conditional variances can be estimated by minimum norm quadratic unbiased estimation (MINQUE) method. An adjusted unbiased prediction (AUP) procedure is suggested for predicting conditional genetic effects. A worked example from cotton fruiting data is given for comparison of unconditional and conditional genetic variances and additive effects.

  13. Application of computational intelligence techniques for load shedding in power systems: A review

    International Nuclear Information System (INIS)

    Laghari, J.A.; Mokhlis, H.; Bakar, A.H.A.; Mohamad, Hasmaini

    2013-01-01

    Highlights: • The power system blackout history of last two decades is presented. • Conventional load shedding techniques, their types and limitations are presented. • Applications of intelligent techniques in load shedding are presented. • Intelligent techniques include ANN, fuzzy logic, ANFIS, genetic algorithm and PSO. • The discussion and comparison between these techniques are provided. - Abstract: Recent blackouts around the world question the reliability of conventional and adaptive load shedding techniques in avoiding such power outages. To address this issue, reliable techniques are required to provide fast and accurate load shedding to prevent collapse in the power system. Computational intelligence techniques, due to their robustness and flexibility in dealing with complex non-linear systems, could be an option in addressing this problem. Computational intelligence includes techniques like artificial neural networks, genetic algorithms, fuzzy logic control, adaptive neuro-fuzzy inference system, and particle swarm optimization. Research in these techniques is being undertaken in order to discover means for more efficient and reliable load shedding. This paper provides an overview of these techniques as applied to load shedding in a power system. This paper also compares the advantages of computational intelligence techniques over conventional load shedding techniques. Finally, this paper discusses the limitation of computational intelligence techniques, which restricts their usage in load shedding in real time

  14. Genetics, health care, and public policy: an introduction to public health genetics

    National Research Council Canada - National Science Library

    Stewart, Alison

    2007-01-01

    ... initiative About this book Further reading and resources Principles of public health The emergence of public health genetics The human genome project and 'genomic medicine' Community genetics Current developments in public health genetics Genomics and global health 2 Genetic science and technology Basic molecular genetics Genes and the geno...

  15. Genetic testing in the epilepsies—Report of the ILAE Genetics Commission

    OpenAIRE

    Ottman, Ruth; Hirose, Shinichi; Jain, Satish; Lerche, Holger; Lopes-Cendes, Iscia; Noebels, Jeffrey L.; Serratosa, José; Zara, Federico; Scheffer, Ingrid E.

    2010-01-01

    In this report, the International League Against Epilepsy (ILAE) Genetics Commission discusses essential issues to be considered with regard to clinical genetic testing in the epilepsies. Genetic research on the epilepsies has led to the identification of more than 20 genes with a major effect on susceptibility to idiopathic epilepsies. The most important potential clinical application of these discoveries is genetic testing: the use of genetic information, either to clarify the diagnosis in ...

  16. Molecular Population Genetics.

    Science.gov (United States)

    Casillas, Sònia; Barbadilla, Antonio

    2017-03-01

    Molecular population genetics aims to explain genetic variation and molecular evolution from population genetics principles. The field was born 50 years ago with the first measures of genetic variation in allozyme loci, continued with the nucleotide sequencing era, and is currently in the era of population genomics. During this period, molecular population genetics has been revolutionized by progress in data acquisition and theoretical developments. The conceptual elegance of the neutral theory of molecular evolution or the footprint carved by natural selection on the patterns of genetic variation are two examples of the vast number of inspiring findings of population genetics research. Since the inception of the field, Drosophila has been the prominent model species: molecular variation in populations was first described in Drosophila and most of the population genetics hypotheses were tested in Drosophila species. In this review, we describe the main concepts, methods, and landmarks of molecular population genetics, using the Drosophila model as a reference. We describe the different genetic data sets made available by advances in molecular technologies, and the theoretical developments fostered by these data. Finally, we review the results and new insights provided by the population genomics approach, and conclude by enumerating challenges and new lines of inquiry posed by increasingly large population scale sequence data. Copyright © 2017 Casillas and Barbadilla.

  17. A Systematic Bayesian Integration of Epidemiological and Genetic Data

    Science.gov (United States)

    Lau, Max S. Y.; Marion, Glenn; Streftaris, George; Gibson, Gavin

    2015-01-01

    Genetic sequence data on pathogens have great potential to inform inference of their transmission dynamics ultimately leading to better disease control. Where genetic change and disease transmission occur on comparable timescales additional information can be inferred via the joint analysis of such genetic sequence data and epidemiological observations based on clinical symptoms and diagnostic tests. Although recently introduced approaches represent substantial progress, for computational reasons they approximate genuine joint inference of disease dynamics and genetic change in the pathogen population, capturing partially the joint epidemiological-evolutionary dynamics. Improved methods are needed to fully integrate such genetic data with epidemiological observations, for achieving a more robust inference of the transmission tree and other key epidemiological parameters such as latent periods. Here, building on current literature, a novel Bayesian framework is proposed that infers simultaneously and explicitly the transmission tree and unobserved transmitted pathogen sequences. Our framework facilitates the use of realistic likelihood functions and enables systematic and genuine joint inference of the epidemiological-evolutionary process from partially observed outbreaks. Using simulated data it is shown that this approach is able to infer accurately joint epidemiological-evolutionary dynamics, even when pathogen sequences and epidemiological data are incomplete, and when sequences are available for only a fraction of exposures. These results also characterise and quantify the value of incomplete and partial sequence data, which has important implications for sampling design, and demonstrate the abilities of the introduced method to identify multiple clusters within an outbreak. The framework is used to analyse an outbreak of foot-and-mouth disease in the UK, enhancing current understanding of its transmission dynamics and evolutionary process. PMID:26599399

  18. Non-linear nuclear engineering models as genetic programming application

    International Nuclear Information System (INIS)

    Domingos, Roberto P.; Schirru, Roberto; Martinez, Aquilino S.

    1997-01-01

    This work presents a Genetic Programming paradigm and a nuclear application. A field of Artificial Intelligence, based on the concepts of Species Evolution and Natural Selection, can be understood as a self-programming process where the computer is the main agent responsible for the discovery of a program able to solve a given problem. In the present case, the problem was to find a mathematical expression in symbolic form, able to express the existent relation between equivalent ratio of a fuel cell, the enrichment of fuel elements and the multiplication factor. Such expression would avoid repeatedly reactor physics codes execution for core optimization. The results were compared with those obtained by different techniques such as Neural Networks and Linear Multiple Regression. Genetic Programming has shown to present a performance as good as, and under some features superior to Neural Network and Linear Multiple Regression. (author). 10 refs., 8 figs., 1 tabs

  19. Computational approaches to identify functional genetic variants in cancer genomes

    DEFF Research Database (Denmark)

    Gonzalez-Perez, Abel; Mustonen, Ville; Reva, Boris

    2013-01-01

    The International Cancer Genome Consortium (ICGC) aims to catalog genomic abnormalities in tumors from 50 different cancer types. Genome sequencing reveals hundreds to thousands of somatic mutations in each tumor but only a minority of these drive tumor progression. We present the result of discu......The International Cancer Genome Consortium (ICGC) aims to catalog genomic abnormalities in tumors from 50 different cancer types. Genome sequencing reveals hundreds to thousands of somatic mutations in each tumor but only a minority of these drive tumor progression. We present the result...... of discussions within the ICGC on how to address the challenge of identifying mutations that contribute to oncogenesis, tumor maintenance or response to therapy, and recommend computational techniques to annotate somatic variants and predict their impact on cancer phenotype....

  20. Computer science of the high performance; Informatica del alto rendimiento

    Energy Technology Data Exchange (ETDEWEB)

    Moraleda, A.

    2008-07-01

    The high performance computing is taking shape as a powerful accelerator of the process of innovation, to drastically reduce the waiting times for access to the results and the findings in a growing number of processes and activities as complex and important as medicine, genetics, pharmacology, environment, natural resources management or the simulation of complex processes in a wide variety of industries. (Author)

  1. Knowledge of Genetics and Attitudes toward Genetic Testing among College Students in Saudi Arabia.

    Science.gov (United States)

    Olwi, Duaa; Merdad, Leena; Ramadan, Eman

    2016-01-01

    Genetic testing has been gradually permeating the practice of medicine. Health-care providers may be confronted with new genetic approaches that require genetically informed decisions which will be influenced by patients' knowledge of genetics and their attitudes toward genetic testing. This study assesses the knowledge of genetics and attitudes toward genetic testing among college students. A cross-sectional study was conducted using a multistage stratified sample of 920 senior college students enrolled at King Abdulaziz University, Saudi Arabia. Information regarding knowledge of genetics, attitudes toward genetic testing, and sociodemographic data were collected using a self-administered questionnaire. In general, students had a good knowledge of genetics but lacked some fundamentals of genetics. The majority of students showed positive attitudes toward genetic testing, but some students showed negative attitudes toward certain aspects of genetic testing such as resorting to abortion in the case of an untreatable major genetic defect in an unborn fetus. The main significant predictors of knowledge were faculty, gender, academic year, and some prior awareness of 'genetic testing'. The main significant predictors of attitudes were gender, academic year, grade point average, and some prior awareness of 'genetic testing'. The knowledge of genetics among college students was higher than has been reported in other studies, and the attitudes toward genetic testing were fairly positive. Genetics educational programs that target youths may improve knowledge of genetics and create a public perception that further supports genetic testing. © 2016 S. Karger AG, Basel.

  2. Deep Learning for Population Genetic Inference.

    Directory of Open Access Journals (Sweden)

    Sara Sheehan

    2016-03-01

    Full Text Available Given genomic variation data from multiple individuals, computing the likelihood of complex population genetic models is often infeasible. To circumvent this problem, we introduce a novel likelihood-free inference framework by applying deep learning, a powerful modern technique in machine learning. Deep learning makes use of multilayer neural networks to learn a feature-based function from the input (e.g., hundreds of correlated summary statistics of data to the output (e.g., population genetic parameters of interest. We demonstrate that deep learning can be effectively employed for population genetic inference and learning informative features of data. As a concrete application, we focus on the challenging problem of jointly inferring natural selection and demography (in the form of a population size change history. Our method is able to separate the global nature of demography from the local nature of selection, without sequential steps for these two factors. Studying demography and selection jointly is motivated by Drosophila, where pervasive selection confounds demographic analysis. We apply our method to 197 African Drosophila melanogaster genomes from Zambia to infer both their overall demography, and regions of their genome under selection. We find many regions of the genome that have experienced hard sweeps, and fewer under selection on standing variation (soft sweep or balancing selection. Interestingly, we find that soft sweeps and balancing selection occur more frequently closer to the centromere of each chromosome. In addition, our demographic inference suggests that previously estimated bottlenecks for African Drosophila melanogaster are too extreme.

  3. Deep Learning for Population Genetic Inference

    Science.gov (United States)

    Sheehan, Sara; Song, Yun S.

    2016-01-01

    Given genomic variation data from multiple individuals, computing the likelihood of complex population genetic models is often infeasible. To circumvent this problem, we introduce a novel likelihood-free inference framework by applying deep learning, a powerful modern technique in machine learning. Deep learning makes use of multilayer neural networks to learn a feature-based function from the input (e.g., hundreds of correlated summary statistics of data) to the output (e.g., population genetic parameters of interest). We demonstrate that deep learning can be effectively employed for population genetic inference and learning informative features of data. As a concrete application, we focus on the challenging problem of jointly inferring natural selection and demography (in the form of a population size change history). Our method is able to separate the global nature of demography from the local nature of selection, without sequential steps for these two factors. Studying demography and selection jointly is motivated by Drosophila, where pervasive selection confounds demographic analysis. We apply our method to 197 African Drosophila melanogaster genomes from Zambia to infer both their overall demography, and regions of their genome under selection. We find many regions of the genome that have experienced hard sweeps, and fewer under selection on standing variation (soft sweep) or balancing selection. Interestingly, we find that soft sweeps and balancing selection occur more frequently closer to the centromere of each chromosome. In addition, our demographic inference suggests that previously estimated bottlenecks for African Drosophila melanogaster are too extreme. PMID:27018908

  4. Invasion genetics of the introduced black rat (Rattus rattus) in Senegal, West Africa

    Czech Academy of Sciences Publication Activity Database

    Konečný, Adam; Estoup, A.; Duplantier, J.-M.; Bryja, Josef; Ba, K.; Galan, M.; Tatard, C.; Cosson, J.-F.

    2013-01-01

    Roč. 22, č. 2 (2013), s. 286-300 ISSN 0962-1083 R&D Project s: GA AV ČR IAA6093404; GA ČR GAP506/10/0983 Institutional support: RVO:68081766 Keywords : approximate bayesian computation * bioinvasion * Bayesian clustering * founder effects * genetic admixture * microsatellites * multiple introductions Subject RIV: EG - Zoology Impact factor: 5.840, year: 2013

  5. Preimplantation genetic screening.

    Science.gov (United States)

    Harper, Joyce C

    2018-03-01

    Preimplantation genetic diagnosis was first successfully performed in 1989 as an alternative to prenatal diagnosis for couples at risk of transmitting a genetic or chromosomal abnormality, such as cystic fibrosis, to their child. From embryos generated in vitro, biopsied cells are genetically tested. From the mid-1990s, this technology has been employed as an embryo selection tool for patients undergoing in vitro fertilisation, screening as many chromosomes as possible, in the hope that selecting chromosomally normal embryos will lead to higher implantation and decreased miscarriage rates. This procedure, preimplantation genetic screening, was initially performed using fluorescent in situ hybridisation, but 11 randomised controlled trials of screening using this technique showed no improvement in in vitro fertilisation delivery rates. Progress in genetic testing has led to the introduction of array comparative genomic hybridisation, quantitative polymerase chain reaction, and next generation sequencing for preimplantation genetic screening, and three small randomised controlled trials of preimplantation genetic screening using these new techniques indicate a modest benefit. Other trials are still in progress but, regardless of their results, preimplantation genetic screening is now being offered globally. In the near future, it is likely that sequencing will be used to screen the full genetic code of the embryo.

  6. Genetic algorithms and Monte Carlo simulation for optimal plant design

    International Nuclear Information System (INIS)

    Cantoni, M.; Marseguerra, M.; Zio, E.

    2000-01-01

    We present an approach to the optimal plant design (choice of system layout and components) under conflicting safety and economic constraints, based upon the coupling of a Monte Carlo evaluation of plant operation with a Genetic Algorithms-maximization procedure. The Monte Carlo simulation model provides a flexible tool, which enables one to describe relevant aspects of plant design and operation, such as standby modes and deteriorating repairs, not easily captured by analytical models. The effects of deteriorating repairs are described by means of a modified Brown-Proschan model of imperfect repair which accounts for the possibility of an increased proneness to failure of a component after a repair. The transitions of a component from standby to active, and vice versa, are simulated using a multiplicative correlation model. The genetic algorithms procedure is demanded to optimize a profit function which accounts for the plant safety and economic performance and which is evaluated, for each possible design, by the above Monte Carlo simulation. In order to avoid an overwhelming use of computer time, for each potential solution proposed by the genetic algorithm, we perform only few hundreds Monte Carlo histories and, then, exploit the fact that during the genetic algorithm population evolution, the fit chromosomes appear repeatedly many times, so that the results for the solutions of interest (i.e. the best ones) attain statistical significance

  7. Genetic diversity of sesame (sesamum indicum L.) germplasm from Pakistan using RAPD markers

    Energy Technology Data Exchange (ETDEWEB)

    Akbar, F; Rabbani, M A; Masood, M S; Shinwari, Z.K., E-mail: shinwari@qau.edu.p

    2011-08-15

    Genetic diversity among 20 sesame (Sesamum indicum L.) accessions was examined at DNA level by means of random amplified polymorphic DNA (RAPD) analysis. Ten primers used produced a total of 93 RAPD fragments, of which 70 (75%) were polymorphic. Each primer generated 5 to 17 amplified fragments with an average of 9.3 bands per primer. Based on pair-wise comparisons of RAPD amplification products, Nei and Li's similarity coefficients were computed to assess the associations among the accessions. Pair-wise similarity indices varied from 0.65 to 0.91. A UPGMA cluster analysis based on these genetic similarities located most of the accessions far apart from one another, showing a high level of polymorphism. Genetically, all the genotypes were classified into two major groups and six subgroups or clusters. A single accession (22243) was relatively distinct from rest of the accessions and created independent cluster. In conclusion, even with the use of a limited set of primers, RAPD technique revealed a high level of genetic variation among sesame accessions collected from diverse ecologies of Pakistan. This high level of genetic diversity among the genotypes suggested that RAPD technique is valuable for sesame systematic, and can be helpful for the upholding of germplasm banks and the competent choice of parents in breeding programs. (author)

  8. Genetic diversity of sesame (sesamum indicum L.) germplasm from Pakistan using RAPD markers

    International Nuclear Information System (INIS)

    Akbar, F; Rabbani, M.A.; Masood, M.S.; Shinwari, Z.K.

    2011-01-01

    Genetic diversity among 20 sesame (Sesamum indicum L.) accessions was examined at DNA level by means of random amplified polymorphic DNA (RAPD) analysis. Ten primers used produced a total of 93 RAPD fragments, of which 70 (75%) were polymorphic. Each primer generated 5 to 17 amplified fragments with an average of 9.3 bands per primer. Based on pair-wise comparisons of RAPD amplification products, Nei and Li's similarity coefficients were computed to assess the associations among the accessions. Pair-wise similarity indices varied from 0.65 to 0.91. A UPGMA cluster analysis based on these genetic similarities located most of the accessions far apart from one another, showing a high level of polymorphism. Genetically, all the genotypes were classified into two major groups and six subgroups or clusters. A single accession (22243) was relatively distinct from rest of the accessions and created independent cluster. In conclusion, even with the use of a limited set of primers, RAPD technique revealed a high level of genetic variation among sesame accessions collected from diverse ecologies of Pakistan. This high level of genetic diversity among the genotypes suggested that RAPD technique is valuable for sesame systematic, and can be helpful for the upholding of germplasm banks and the competent choice of parents in breeding programs. (author)

  9. Evaluating genetic ancestry and self-reported ethnicity in the context of carrier screening.

    Science.gov (United States)

    Shraga, Roman; Yarnall, Sarah; Elango, Sonya; Manoharan, Arun; Rodriguez, Sally Ann; Bristow, Sara L; Kumar, Neha; Niknazar, Mohammad; Hoffman, David; Ghadir, Shahin; Vassena, Rita; Chen, Serena H; Hershlag, Avner; Grifo, Jamie; Puig, Oscar

    2017-11-28

    Current professional society guidelines recommend genetic carrier screening be offered on the basis of ethnicity, or when using expanded carrier screening panels, they recommend to compute residual risk based on ethnicity. We investigated the reliability of self-reported ethnicity in 9138 subjects referred to carrier screening. Self-reported ethnicity gathered from test requisition forms and during post-test genetic counseling, and genetic ancestry predicted by a statistical model, were compared for concordance. We identified several discrepancies between the two sources of self-reported ethnicity and genetic ancestry. Only 30.3% of individuals who indicated Mediterranean ancestry during consultation self-reported this on requisition forms. Additionally, the proportion of individuals who reported Southeast Asian but were estimated to have a different genetic ancestry was found to depend on the source of self-report. Finally, individuals who reported Latin American demonstrated a high degree of ancestral admixture. As a result, carrier rates and residual risks provided for patient decision-making are impacted if using self-reported ethnicity. Our analysis highlights the unreliability of ethnicity classification based on patient self-reports. We recommend the routine use of pan-ethnic carrier screening panels in reproductive medicine. Furthermore, the use of an ancestry model would allow better estimation of carrier rates and residual risks.

  10. Intelligent systems and soft computing for nuclear science and industry

    International Nuclear Information System (INIS)

    Ruan, D.; D'hondt, P.; Govaerts, P.; Kerre, E.E.

    1996-01-01

    The second international workshop on Fuzzy Logic and Intelligent Technologies in Nuclear Science (FLINS) addresses topics related to intelligent systems and soft computing for nuclear science and industry. The proceedings contain 52 papers in different fields such as radiation protection, nuclear safety (human factors and reliability), safeguards, nuclear reactor control, production processes in the fuel cycle, dismantling, waste and disposal, decision making, and nuclear reactor control. A clear link is made between theory and applications of fuzzy logic such as neural networks, expert systems, robotics, man-machine interfaces, and decision-support techniques by using modern and advanced technologies and tools. The papers are grouped in three sections. The first section (Soft computing techniques) deals with basic tools to treat fuzzy logic, neural networks, genetic algorithms, decision-making, and software used for general soft-computing aspects. The second section (Intelligent engineering systems) includes contributions on engineering problems such as knowledge-based engineering, expert systems, process control integration, diagnosis, measurements, and interpretation by soft computing. The third section (Nuclear applications) focusses on the application of soft computing and intelligent systems in nuclear science and industry

  11. Genetic Local Search for Optimum Multiuser Detection Problem in DS-CDMA Systems

    Science.gov (United States)

    Wang, Shaowei; Ji, Xiaoyong

    Optimum multiuser detection (OMD) in direct-sequence code-division multiple access (DS-CDMA) systems is an NP-complete problem. In this paper, we present a genetic local search algorithm, which consists of an evolution strategy framework and a local improvement procedure. The evolution strategy searches the space of feasible, locally optimal solutions only. A fast iterated local search algorithm, which employs the proprietary characteristics of the OMD problem, produces local optima with great efficiency. Computer simulations show the bit error rate (BER) performance of the GLS outperforms other multiuser detectors in all cases discussed. The computation time is polynomial complexity in the number of users.

  12. Selfish genetic elements, genetic conflict, and evolutionary innovation.

    Science.gov (United States)

    Werren, John H

    2011-06-28

    Genomes are vulnerable to selfish genetic elements (SGEs), which enhance their own transmission relative to the rest of an individual's genome but are neutral or harmful to the individual as a whole. As a result, genetic conflict occurs between SGEs and other genetic elements in the genome. There is growing evidence that SGEs, and the resulting genetic conflict, are an important motor for evolutionary change and innovation. In this review, the kinds of SGEs and their evolutionary consequences are described, including how these elements shape basic biological features, such as genome structure and gene regulation, evolution of new genes, origin of new species, and mechanisms of sex determination and development. The dynamics of SGEs are also considered, including possible "evolutionary functions" of SGEs.

  13. EvolQG - An R package for evolutionary quantitative genetics [version 2; referees: 1 approved, 2 approved with reservations

    Directory of Open Access Journals (Sweden)

    Diogo Melo

    2016-06-01

    Full Text Available We present an open source package for performing evolutionary quantitative genetics analyses in the R environment for statistical computing. Evolutionary theory shows that evolution depends critically on the available variation in a given population. When dealing with many quantitative traits this variation is expressed in the form of a covariance matrix, particularly the additive genetic covariance matrix or sometimes the phenotypic matrix, when the genetic matrix is unavailable and there is evidence the phenotypic matrix is sufficiently similar to the genetic matrix. Given this mathematical representation of available variation, the EvolQG package provides functions for calculation of relevant evolutionary statistics; estimation of sampling error; corrections for this error; matrix comparison via correlations, distances and matrix decomposition; analysis of modularity patterns; and functions for testing evolutionary hypotheses on taxa diversification.

  14. Codon size reduction as the origin of the triplet genetic code.

    Directory of Open Access Journals (Sweden)

    Pavel V Baranov

    Full Text Available The genetic code appears to be optimized in its robustness to missense errors and frameshift errors. In addition, the genetic code is near-optimal in terms of its ability to carry information in addition to the sequences of encoded proteins. As evolution has no foresight, optimality of the modern genetic code suggests that it evolved from less optimal code variants. The length of codons in the genetic code is also optimal, as three is the minimal nucleotide combination that can encode the twenty standard amino acids. The apparent impossibility of transitions between codon sizes in a discontinuous manner during evolution has resulted in an unbending view that the genetic code was always triplet. Yet, recent experimental evidence on quadruplet decoding, as well as the discovery of organisms with ambiguous and dual decoding, suggest that the possibility of the evolution of triplet decoding from living systems with non-triplet decoding merits reconsideration and further exploration. To explore this possibility we designed a mathematical model of the evolution of primitive digital coding systems which can decode nucleotide sequences into protein sequences. These coding systems can evolve their nucleotide sequences via genetic events of Darwinian evolution, such as point-mutations. The replication rates of such coding systems depend on the accuracy of the generated protein sequences. Computer simulations based on our model show that decoding systems with codons of length greater than three spontaneously evolve into predominantly triplet decoding systems. Our findings suggest a plausible scenario for the evolution of the triplet genetic code in a continuous manner. This scenario suggests an explanation of how protein synthesis could be accomplished by means of long RNA-RNA interactions prior to the emergence of the complex decoding machinery, such as the ribosome, that is required for stabilization and discrimination of otherwise weak triplet codon

  15. Defining the computational structure of the motion detector in Drosophila.

    Science.gov (United States)

    Clark, Damon A; Bursztyn, Limor; Horowitz, Mark A; Schnitzer, Mark J; Clandinin, Thomas R

    2011-06-23

    Many animals rely on visual motion detection for survival. Motion information is extracted from spatiotemporal intensity patterns on the retina, a paradigmatic neural computation. A phenomenological model, the Hassenstein-Reichardt correlator (HRC), relates visual inputs to neural activity and behavioral responses to motion, but the circuits that implement this computation remain unknown. By using cell-type specific genetic silencing, minimal motion stimuli, and in vivo calcium imaging, we examine two critical HRC inputs. These two pathways respond preferentially to light and dark moving edges. We demonstrate that these pathways perform overlapping but complementary subsets of the computations underlying the HRC. A numerical model implementing differential weighting of these operations displays the observed edge preferences. Intriguingly, these pathways are distinguished by their sensitivities to a stimulus correlation that corresponds to an illusory percept, "reverse phi," that affects many species. Thus, this computational architecture may be widely used to achieve edge selectivity in motion detection. Copyright © 2011 Elsevier Inc. All rights reserved.

  16. Multigene Genetic Programming for Estimation of Elastic Modulus of Concrete

    Directory of Open Access Journals (Sweden)

    Alireza Mohammadi Bayazidi

    2014-01-01

    Full Text Available This paper presents a new multigene genetic programming (MGGP approach for estimation of elastic modulus of concrete. The MGGP technique models the elastic modulus behavior by integrating the capabilities of standard genetic programming and classical regression. The main aim is to derive precise relationships between the tangent elastic moduli of normal and high strength concrete and the corresponding compressive strength values. Another important contribution of this study is to develop a generalized prediction model for the elastic moduli of both normal and high strength concrete. Numerous concrete compressive strength test results are obtained from the literature to develop the models. A comprehensive comparative study is conducted to verify the performance of the models. The proposed models perform superior to the existing traditional models, as well as those derived using other powerful soft computing tools.

  17. The role of soft computing in intelligent machines.

    Science.gov (United States)

    de Silva, Clarence W

    2003-08-15

    An intelligent machine relies on computational intelligence in generating its intelligent behaviour. This requires a knowledge system in which representation and processing of knowledge are central functions. Approximation is a 'soft' concept, and the capability to approximate for the purposes of comparison, pattern recognition, reasoning, and decision making is a manifestation of intelligence. This paper examines the use of soft computing in intelligent machines. Soft computing is an important branch of computational intelligence, where fuzzy logic, probability theory, neural networks, and genetic algorithms are synergistically used to mimic the reasoning and decision making of a human. This paper explores several important characteristics and capabilities of machines that exhibit intelligent behaviour. Approaches that are useful in the development of an intelligent machine are introduced. The paper presents a general structure for an intelligent machine, giving particular emphasis to its primary components, such as sensors, actuators, controllers, and the communication backbone, and their interaction. The role of soft computing within the overall system is discussed. Common techniques and approaches that will be useful in the development of an intelligent machine are introduced, and the main steps in the development of an intelligent machine for practical use are given. An industrial machine, which employs the concepts of soft computing in its operation, is presented, and one aspect of intelligent tuning, which is incorporated into the machine, is illustrated.

  18. In vivo formation and repair of DNA double-strand breaks after computed tomography examinations

    OpenAIRE

    Löbrich, Markus; Rief, Nicole; Kühne, Martin; Heckmann, Martina; Fleckenstein, Jochen; Rübe, Christian; Uder, Michael

    2005-01-01

    Ionizing radiation can lead to a variety of deleterious effects in humans, most importantly to the induction of cancer. DNA double-strand breaks (DSBs) are among the most significant genetic lesions introduced by ionizing radiation that can initiate carcinogenesis. We have enumerated γ-H2AX foci as a measure for DSBs in lymphocytes from individuals undergoing computed tomography examination of the thorax and/or the abdomen. The number of DSBs induced by computed tomography examination was fou...

  19. Coevolution of Artificial Agents Using Evolutionary Computation in Bargaining Game

    Directory of Open Access Journals (Sweden)

    Sangwook Lee

    2015-01-01

    Full Text Available Analysis of bargaining game using evolutionary computation is essential issue in the field of game theory. This paper investigates the interaction and coevolutionary process among heterogeneous artificial agents using evolutionary computation (EC in the bargaining game. In particular, the game performance with regard to payoff through the interaction and coevolution of agents is studied. We present three kinds of EC based agents (EC-agent participating in the bargaining game: genetic algorithm (GA, particle swarm optimization (PSO, and differential evolution (DE. The agents’ performance with regard to changing condition is compared. From the simulation results it is found that the PSO-agent is superior to the other agents.

  20. Cryptic Genetic Variation in Evolutionary Developmental Genetics

    Directory of Open Access Journals (Sweden)

    Annalise B. Paaby

    2016-06-01

    Full Text Available Evolutionary developmental genetics has traditionally been conducted by two groups: Molecular evolutionists who emphasize divergence between species or higher taxa, and quantitative geneticists who study variation within species. Neither approach really comes to grips with the complexities of evolutionary transitions, particularly in light of the realization from genome-wide association studies that most complex traits fit an infinitesimal architecture, being influenced by thousands of loci. This paper discusses robustness, plasticity and lability, phenomena that we argue potentiate major evolutionary changes and provide a bridge between the conceptual treatments of macro- and micro-evolution. We offer cryptic genetic variation and conditional neutrality as mechanisms by which standing genetic variation can lead to developmental system drift and, sheltered within canalized processes, may facilitate developmental transitions and the evolution of novelty. Synthesis of the two dominant perspectives will require recognition that adaptation, divergence, drift and stability all depend on similar underlying quantitative genetic processes—processes that cannot be fully observed in continuously varying visible traits.

  1. Charting the genotype-phenotype map: lessons from the Drosophila melanogaster Genetic Reference Panel.

    Science.gov (United States)

    Mackay, Trudy F C; Huang, Wen

    2018-01-01

    Understanding the genetic architecture (causal molecular variants, their effects, and frequencies) of quantitative traits is important for precision agriculture and medicine and predicting adaptive evolution, but is challenging in most species. The Drosophila melanogaster Genetic Reference Panel (DGRP) is a collection of 205 inbred strains with whole genome sequences derived from a single wild population in Raleigh, NC, USA. The large amount of quantitative genetic variation, lack of population structure, and rapid local decay of linkage disequilibrium in the DGRP and outbred populations derived from DGRP lines present a favorable scenario for performing genome-wide association (GWA) mapping analyses to identify candidate causal genes, polymorphisms, and pathways affecting quantitative traits. The many GWA studies utilizing the DGRP have revealed substantial natural genetic variation for all reported traits, little evidence for variants with large effects but enrichment for variants with low P-values, and a tendency for lower frequency variants to have larger effects than more common variants. The variants detected in the GWA analyses rarely overlap those discovered using mutagenesis, and often are the first functional annotations of computationally predicted genes. Variants implicated in GWA analyses typically have sex-specific and genetic background-specific (epistatic) effects, as well as pleiotropic effects on other quantitative traits. Studies in the DGRP reveal substantial genetic control of environmental variation. Taking account of genetic architecture can greatly improve genomic prediction in the DGRP. These features of the genetic architecture of quantitative traits are likely to apply to other species, including humans. WIREs Dev Biol 2018, 7:e289. doi: 10.1002/wdev.289 This article is categorized under: Invertebrate Organogenesis > Flies. © 2017 Wiley Periodicals, Inc.

  2. Protecting genetic privacy.

    Science.gov (United States)

    Roche, P A; Annas, G J

    2001-05-01

    This article outlines the arguments for and against new rules to protect genetic privacy. We explain why genetic information is different to other sensitive medical information, why researchers and biotechnology companies have opposed new rules to protect genetic privacy (and favour anti-discrimination laws instead), and discuss what can be done to protect privacy in relation to genetic-sequence information and to DNA samples themselves.

  3. Real Time Optima Tracking Using Harvesting Models of the Genetic Algorithm

    Science.gov (United States)

    Baskaran, Subbiah; Noever, D.

    1999-01-01

    Tracking optima in real time propulsion control, particularly for non-stationary optimization problems is a challenging task. Several approaches have been put forward for such a study including the numerical method called the genetic algorithm. In brief, this approach is built upon Darwinian-style competition between numerical alternatives displayed in the form of binary strings, or by analogy to 'pseudogenes'. Breeding of improved solution is an often cited parallel to natural selection in.evolutionary or soft computing. In this report we present our results of applying a novel model of a genetic algorithm for tracking optima in propulsion engineering and in real time control. We specialize the algorithm to mission profiling and planning optimizations, both to select reduced propulsion needs through trajectory planning and to explore time or fuel conservation strategies.

  4. Computational analysis of candidate disease genes and variants for Salt-sensitive hypertension in indigenous Southern Africans

    KAUST Repository

    Tiffin, Nicki; Meintjes, Ayton; Ramesar, Rajkumar; Bajic, Vladimir B.; Rayner, Brian

    2010-01-01

    appears more prevalent in people of indigenous African origin. The underlying genetics of salt-sensitive hypertension, however, are poorly understood. In this study, computational methods including text- and data-mining have been used to select

  5. The genetic difference principle.

    Science.gov (United States)

    Farrelly, Colin

    2004-01-01

    In the newly emerging debates about genetics and justice three distinct principles have begun to emerge concerning what the distributive aim of genetic interventions should be. These principles are: genetic equality, a genetic decent minimum, and the genetic difference principle. In this paper, I examine the rationale of each of these principles and argue that genetic equality and a genetic decent minimum are ill-equipped to tackle what I call the currency problem and the problem of weight. The genetic difference principle is the most promising of the three principles and I develop this principle so that it takes seriously the concerns of just health care and distributive justice in general. Given the strains on public funds for other important social programmes, the costs of pursuing genetic interventions and the nature of genetic interventions, I conclude that a more lax interpretation of the genetic difference principle is appropriate. This interpretation stipulates that genetic inequalities should be arranged so that they are to the greatest reasonable benefit of the least advantaged. Such a proposal is consistent with prioritarianism and provides some practical guidance for non-ideal societies--that is, societies that do not have the endless amount of resources needed to satisfy every requirement of justice.

  6. A systematic approach to assessing the clinical significance of genetic variants.

    Science.gov (United States)

    Duzkale, H; Shen, J; McLaughlin, H; Alfares, A; Kelly, M A; Pugh, T J; Funke, B H; Rehm, H L; Lebo, M S

    2013-11-01

    Molecular genetic testing informs diagnosis, prognosis, and risk assessment for patients and their family members. Recent advances in low-cost, high-throughput DNA sequencing and computing technologies have enabled the rapid expansion of genetic test content, resulting in dramatically increased numbers of DNA variants identified per test. To address this challenge, our laboratory has developed a systematic approach to thorough and efficient assessments of variants for pathogenicity determination. We first search for existing data in publications and databases including internal, collaborative and public resources. We then perform full evidence-based assessments through statistical analyses of observations in the general population and disease cohorts, evaluation of experimental data from in vivo or in vitro studies, and computational predictions of potential impacts of each variant. Finally, we weigh all evidence to reach an overall conclusion on the potential for each variant to be disease causing. In this report, we highlight the principles of variant assessment, address the caveats and pitfalls, and provide examples to illustrate the process. By sharing our experience and providing a framework for variant assessment, including access to a freely available customizable tool, we hope to help move towards standardized and consistent approaches to variant assessment. © 2013 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

  7. Preimplantation Genetic Screening and Preimplantation Genetic Diagnosis.

    Science.gov (United States)

    Sullivan-Pyke, Chantae; Dokras, Anuja

    2018-03-01

    Preimplantation genetic testing encompasses preimplantation genetic screening (PGS) and preimplantation genetic diagnosis (PGD). PGS improves success rates of in vitro fertilization by ensuring the transfer of euploid embryos that have a higher chance of implantation and resulting in a live birth. PGD enables the identification of embryos with specific disease-causing mutations and transfer of unaffected embryos. The development of whole genome amplification and genomic tools, including single nucleotide polymorphism microarrays, comparative genomic hybridization microarrays, and next-generation sequencing, has led to faster, more accurate diagnoses that translate to improved pregnancy and live birth rates. Copyright © 2017 Elsevier Inc. All rights reserved.

  8. Bacterial Population Genetics in a Forensic Context

    Energy Technology Data Exchange (ETDEWEB)

    Velsko, S P

    2009-11-02

    networks involve complex soil communities and metapopulations about which very little is known. It is not clear that these pathogens can be brought into the inference-on-networks framework without additional conceptual advances. (11) For all 8 bacteria some combination of field studies, computational modeling, and laboratory experiments are needed to provide a useful forensic capability for bacterial genetic inference.

  9. A Genetic Algorithm for Feeding Trajectory Optimisation of Fed-batch Fermentation Processes

    Directory of Open Access Journals (Sweden)

    Stoyan Tzonkov

    2009-03-01

    Full Text Available In this work a genetic algorithm is proposed with the purpose of the feeding trajectory optimization during a fed-batch fermentation of E. coli. The feed rate profiles are evaluated based on a number of objective functions. Optimization results obtained for different feeding trajectories demonstrate that the genetic algorithm works well and shows good computational performance. Developed optimal feed profiles meet the defined criteria. The ration of the substrate concentration and the difference between actual cell concentration and theoretical maximum cell concentration is defined as the most appropriate objective function. In this case the final cell concentration of 43 g·l-1 and final product concentration of 125 g·l-1 are achieved and there is not significant excess of substrate.

  10. Marker-based estimation of genetic parameters in genomics.

    Directory of Open Access Journals (Sweden)

    Zhiqiu Hu

    Full Text Available Linear mixed model (LMM analysis has been recently used extensively for estimating additive genetic variances and narrow-sense heritability in many genomic studies. While the LMM analysis is computationally less intensive than the Bayesian algorithms, it remains infeasible for large-scale genomic data sets. In this paper, we advocate the use of a statistical procedure known as symmetric differences squared (SDS as it may serve as a viable alternative when the LMM methods have difficulty or fail to work with large datasets. The SDS procedure is a general and computationally simple method based only on the least squares regression analysis. We carry out computer simulations and empirical analyses to compare the SDS procedure with two commonly used LMM-based procedures. Our results show that the SDS method is not as good as the LMM methods for small data sets, but it becomes progressively better and can match well with the precision of estimation by the LMM methods for data sets with large sample sizes. Its major advantage is that with larger and larger samples, it continues to work with the increasing precision of estimation while the commonly used LMM methods are no longer able to work under our current typical computing capacity. Thus, these results suggest that the SDS method can serve as a viable alternative particularly when analyzing 'big' genomic data sets.

  11. In Silico Dynamics: computer simulation in a Virtual Embryo ...

    Science.gov (United States)

    Abstract: Utilizing cell biological information to predict higher order biological processes is a significant challenge in predictive toxicology. This is especially true for highly dynamical systems such as the embryo where morphogenesis, growth and differentiation require precisely orchestrated interactions between diverse cell populations. In patterning the embryo, genetic signals setup spatial information that cells then translate into a coordinated biological response. This can be modeled as ‘biowiring diagrams’ representing genetic signals and responses. Because the hallmark of multicellular organization resides in the ability of cells to interact with one another via well-conserved signaling pathways, multiscale computational (in silico) models that enable these interactions provide a platform to translate cellular-molecular lesions perturbations into higher order predictions. Just as ‘the Cell’ is the fundamental unit of biology so too should it be the computational unit (‘Agent’) for modeling embryogenesis. As such, we constructed multicellular agent-based models (ABM) with ‘CompuCell3D’ (www.compucell3d.org) to simulate kinematics of complex cell signaling networks and enable critical tissue events for use in predictive toxicology. Seeding the ABMs with HTS/HCS data from ToxCast demonstrated the potential to predict, quantitatively, the higher order impacts of chemical disruption at the cellular or biochemical level. This is demonstrate

  12. Computational Methods for Modeling Aptamers and Designing Riboswitches

    Directory of Open Access Journals (Sweden)

    Sha Gong

    2017-11-01

    Full Text Available Riboswitches, which are located within certain noncoding RNA region perform functions as genetic “switches”, regulating when and where genes are expressed in response to certain ligands. Understanding the numerous functions of riboswitches requires computation models to predict structures and structural changes of the aptamer domains. Although aptamers often form a complex structure, computational approaches, such as RNAComposer and Rosetta, have already been applied to model the tertiary (three-dimensional (3D structure for several aptamers. As structural changes in aptamers must be achieved within the certain time window for effective regulation, kinetics is another key point for understanding aptamer function in riboswitch-mediated gene regulation. The coarse-grained self-organized polymer (SOP model using Langevin dynamics simulation has been successfully developed to investigate folding kinetics of aptamers, while their co-transcriptional folding kinetics can be modeled by the helix-based computational method and BarMap approach. Based on the known aptamers, the web server Riboswitch Calculator and other theoretical methods provide a new tool to design synthetic riboswitches. This review will represent an overview of these computational methods for modeling structure and kinetics of riboswitch aptamers and for designing riboswitches.

  13. Cancer Genetics Services Directory

    Science.gov (United States)

    ... Services Directory Cancer Prevention Overview Research NCI Cancer Genetics Services Directory This directory lists professionals who provide services related to cancer genetics (cancer risk assessment, genetic counseling, genetic susceptibility testing, ...

  14. Genetic data for groundfish - Genetics and genomics of northeastern Pacific groundfish

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Conduct genetic analyses of groundfish in the northeastern Pacific, with a focus on population genetics and genomics of rockfishes and sablefish. Genetic data for...

  15. Genetics of breast cancer: Applications to the Mexican population

    Directory of Open Access Journals (Sweden)

    Elad Ziv

    2011-10-01

    Full Text Available Breast cancer research has yielded several important results including the strong susceptibility genes,BRCA1 and BRCA2 and more recently 19 genes and genetic loci that confer a more moderate risk.The pace of discovery is accelerating as genetic technology and computational methods improve. These discoveries will change the way that breast cancer risk is understood in Mexico over the next few decades.La investigación en cáncer de mama ha dado varios resultados importantes incluyendo los genes fuertemente susceptibles, BRCA1 y BRCA2, y más recientemente 19 genes y loci genéticos que confieren un riesgo moderado. El ritmo de los descubrimientos se acelera conforme mejora la tecnología y métodos computacionales.Estosdescubrimientoscambiarán la forma en que la investigación del cáncer es comprendida en México en las próximas décadas.

  16. A parallel attractor-finding algorithm based on Boolean satisfiability for genetic regulatory networks.

    Directory of Open Access Journals (Sweden)

    Wensheng Guo

    Full Text Available In biological systems, the dynamic analysis method has gained increasing attention in the past decade. The Boolean network is the most common model of a genetic regulatory network. The interactions of activation and inhibition in the genetic regulatory network are modeled as a set of functions of the Boolean network, while the state transitions in the Boolean network reflect the dynamic property of a genetic regulatory network. A difficult problem for state transition analysis is the finding of attractors. In this paper, we modeled the genetic regulatory network as a Boolean network and proposed a solving algorithm to tackle the attractor finding problem. In the proposed algorithm, we partitioned the Boolean network into several blocks consisting of the strongly connected components according to their gradients, and defined the connection between blocks as decision node. Based on the solutions calculated on the decision nodes and using a satisfiability solving algorithm, we identified the attractors in the state transition graph of each block. The proposed algorithm is benchmarked on a variety of genetic regulatory networks. Compared with existing algorithms, it achieved similar performance on small test cases, and outperformed it on larger and more complex ones, which happens to be the trend of the modern genetic regulatory network. Furthermore, while the existing satisfiability-based algorithms cannot be parallelized due to their inherent algorithm design, the proposed algorithm exhibits a good scalability on parallel computing architectures.

  17. Fuzzy logic, neural networks, and soft computing

    Science.gov (United States)

    Zadeh, Lofti A.

    1994-01-01

    The past few years have witnessed a rapid growth of interest in a cluster of modes of modeling and computation which may be described collectively as soft computing. The distinguishing characteristic of soft computing is that its primary aims are to achieve tractability, robustness, low cost, and high MIQ (machine intelligence quotient) through an exploitation of the tolerance for imprecision and uncertainty. Thus, in soft computing what is usually sought is an approximate solution to a precisely formulated problem or, more typically, an approximate solution to an imprecisely formulated problem. A simple case in point is the problem of parking a car. Generally, humans can park a car rather easily because the final position of the car is not specified exactly. If it were specified to within, say, a few millimeters and a fraction of a degree, it would take hours or days of maneuvering and precise measurements of distance and angular position to solve the problem. What this simple example points to is the fact that, in general, high precision carries a high cost. The challenge, then, is to exploit the tolerance for imprecision by devising methods of computation which lead to an acceptable solution at low cost. By its nature, soft computing is much closer to human reasoning than the traditional modes of computation. At this juncture, the major components of soft computing are fuzzy logic (FL), neural network theory (NN), and probabilistic reasoning techniques (PR), including genetic algorithms, chaos theory, and part of learning theory. Increasingly, these techniques are used in combination to achieve significant improvement in performance and adaptability. Among the important application areas for soft computing are control systems, expert systems, data compression techniques, image processing, and decision support systems. It may be argued that it is soft computing, rather than the traditional hard computing, that should be viewed as the foundation for artificial

  18. A model based on soil structural aspects describing the fate of genetically modified bacteria in soil

    NARCIS (Netherlands)

    Hoeven, van der N.; Elsas, van J.D.; Heijnen, C.E.

    1996-01-01

    A computer simulation model was developed which describes growth and competition of bacteria in the soil environment. In the model, soil was assumed to contain millions of pores of a few different size classes. An introduced bacterial strain, e.g. a genetically modified micro-organism (GEMMO), was

  19. Comparison of collinearity mitigation techniques used in predicting BLUP breeding values and genetic gains over generations

    CSIR Research Space (South Africa)

    Eatwell, KA

    2011-01-01

    Full Text Available techniques and of two computational numerical precisions on the genetic gains in breeding populations. Multiple-trait, multiple-trial BLUP selection scenarios were run on Eucalyptus grandis (F1, F2 and F3) and Pinus patula (F1 and F2) data, comparing...

  20. High-speed detection of emergent market clustering via an unsupervised parallel genetic algorithm

    Directory of Open Access Journals (Sweden)

    Dieter Hendricks

    2016-02-01

    Full Text Available We implement a master-slave parallel genetic algorithm with a bespoke log-likelihood fitness function to identify emergent clusters within price evolutions. We use graphics processing units (GPUs to implement a parallel genetic algorithm and visualise the results using disjoint minimal spanning trees. We demonstrate that our GPU parallel genetic algorithm, implemented on a commercially available general purpose GPU, is able to recover stock clusters in sub-second speed, based on a subset of stocks in the South African market. This approach represents a pragmatic choice for low-cost, scalable parallel computing and is significantly faster than a prototype serial implementation in an optimised C-based fourth-generation programming language, although the results are not directly comparable because of compiler differences. Combined with fast online intraday correlation matrix estimation from high frequency data for cluster identification, the proposed implementation offers cost-effective, near-real-time risk assessment for financial practitioners.

  1. A Computational Approach From Gene to Structure Analysis of the Human ABCA4 Transporter Involved in Genetic Retinal Diseases.

    Science.gov (United States)

    Trezza, Alfonso; Bernini, Andrea; Langella, Andrea; Ascher, David B; Pires, Douglas E V; Sodi, Andrea; Passerini, Ilaria; Pelo, Elisabetta; Rizzo, Stanislao; Niccolai, Neri; Spiga, Ottavia

    2017-10-01

    The aim of this article is to report the investigation of the structural features of ABCA4, a protein associated with a genetic retinal disease. A new database collecting knowledge of ABCA4 structure may facilitate predictions about the possible functional consequences of gene mutations observed in clinical practice. In order to correlate structural and functional effects of the observed mutations, the structure of mouse P-glycoprotein was used as a template for homology modeling. The obtained structural information and genetic data are the basis of our relational database (ABCA4Database). Sequence variability among all ABCA4-deposited entries was calculated and reported as Shannon entropy score at the residue level. The three-dimensional model of ABCA4 structure was used to locate the spatial distribution of the observed variable regions. Our predictions from structural in silico tools were able to accurately link the functional effects of mutations to phenotype. The development of the ABCA4Database gathers all the available genetic and structural information, yielding a global view of the molecular basis of some retinal diseases. ABCA4 modeled structure provides a molecular basis on which to analyze protein sequence mutations related to genetic retinal disease in order to predict the risk of retinal disease across all possible ABCA4 mutations. Additionally, our ABCA4 predicted structure is a good starting point for the creation of a new data analysis model, appropriate for precision medicine, in order to develop a deeper knowledge network of the disease and to improve the management of patients.

  2. Generation of intervention strategy for a genetic regulatory network represented by a family of Markov Chains.

    Science.gov (United States)

    Berlow, Noah; Pal, Ranadip

    2011-01-01

    Genetic Regulatory Networks (GRNs) are frequently modeled as Markov Chains providing the transition probabilities of moving from one state of the network to another. The inverse problem of inference of the Markov Chain from noisy and limited experimental data is an ill posed problem and often generates multiple model possibilities instead of a unique one. In this article, we address the issue of intervention in a genetic regulatory network represented by a family of Markov Chains. The purpose of intervention is to alter the steady state probability distribution of the GRN as the steady states are considered to be representative of the phenotypes. We consider robust stationary control policies with best expected behavior. The extreme computational complexity involved in search of robust stationary control policies is mitigated by using a sequential approach to control policy generation and utilizing computationally efficient techniques for updating the stationary probability distribution of a Markov chain following a rank one perturbation.

  3. Efficient experimental design of high-fidelity three-qubit quantum gates via genetic programming

    Science.gov (United States)

    Devra, Amit; Prabhu, Prithviraj; Singh, Harpreet; Arvind; Dorai, Kavita

    2018-03-01

    We have designed efficient quantum circuits for the three-qubit Toffoli (controlled-controlled-NOT) and the Fredkin (controlled-SWAP) gate, optimized via genetic programming methods. The gates thus obtained were experimentally implemented on a three-qubit NMR quantum information processor, with a high fidelity. Toffoli and Fredkin gates in conjunction with the single-qubit Hadamard gates form a universal gate set for quantum computing and are an essential component of several quantum algorithms. Genetic algorithms are stochastic search algorithms based on the logic of natural selection and biological genetics and have been widely used for quantum information processing applications. We devised a new selection mechanism within the genetic algorithm framework to select individuals from a population. We call this mechanism the "Luck-Choose" mechanism and were able to achieve faster convergence to a solution using this mechanism, as compared to existing selection mechanisms. The optimization was performed under the constraint that the experimentally implemented pulses are of short duration and can be implemented with high fidelity. We demonstrate the advantage of our pulse sequences by comparing our results with existing experimental schemes and other numerical optimization methods.

  4. Predicting the severity of nuclear power plant transients using nearest neighbors modeling optimized by genetic algorithms on a parallel computer

    International Nuclear Information System (INIS)

    Lin, J.; Bartal, Y.; Uhrig, R.E.

    1995-01-01

    The importance of automatic diagnostic systems for nuclear power plants (NPPs) has been discussed in numerous studies, and various such systems have been proposed. None of those systems were designed to predict the severity of the diagnosed scenario. A classification and severity prediction system for NPP transients is developed. The system is based on nearest neighbors modeling, which is optimized using genetic algorithms. The optimization process is used to determine the most important variables for each of the transient types analyzed. An enhanced version of the genetic algorithms is used in which a local downhill search is performed to further increase the accuracy achieved. The genetic algorithms search was implemented on a massively parallel supercomputer, the KSR1-64, to perform the analysis in a reasonable time. The data for this study were supplied by the high-fidelity simulator of the San Onofre unit 1 pressurized water reactor

  5. Prenatal screening and genetics

    DEFF Research Database (Denmark)

    Alderson, P; Aro, A R; Dragonas, T

    2001-01-01

    Although the term 'genetic screening' has been used for decades, this paper discusses how, in its most precise meaning, genetic screening has not yet been widely introduced. 'Prenatal screening' is often confused with 'genetic screening'. As we show, these terms have different meanings, and we...... examine definitions of the relevant concepts in order to illustrate this point. The concepts are i) prenatal, ii) genetic screening, iii) screening, scanning and testing, iv) maternal and foetal tests, v) test techniques and vi) genetic conditions. So far, prenatal screening has little connection...... with precisely defined genetics. There are benefits but also disadvantages in overstating current links between them in the term genetic screening. Policy making and professional and public understandings about screening could be clarified if the distinct meanings of prenatal screening and genetic screening were...

  6. Genetics Home Reference

    Science.gov (United States)

    ... Page Search Home Health Conditions Genes Chromosomes & mtDNA Resources Help Me Understand Genetics Share: Email Facebook Twitter Genetics Home Reference provides consumer-friendly information about the effects of genetic variation on human health. Health Conditions More than 1,200 health ...

  7. Computer tomographic investigation of ancient Egyptian mummies

    Energy Technology Data Exchange (ETDEWEB)

    Huebner, K H; Pahl, W M

    1981-08-01

    Radiological and computer tomographic examinations of Egyptian mummies have been carried out at the Institute of Anthropology and Human Genetics from 1975 to 1978. These have demonstrated the value of CT in medical archaeology. It enables one to study the soft tissues, the skin (if bandaged), the muscles and any organs retained in situ for magical or religious reason. Measurements of attenuation values indicate the materials which were used for mummifying the skin and organs. Characteristic examples are described and the early results of these examinations are discussed.

  8. Computer tomographic investigation of ancient Egyptian mummies

    International Nuclear Information System (INIS)

    Huebner, K.H.; Pahl, W.M.; Tuebingen Univ.

    1981-01-01

    Radiological and computer tomographic examinations of Egyptian mummies have been carried out at the Institute of Anthropology and Human Genetics from 1975 to 1978. These have demonstrated the value of CT in medical archaeology. It enables one to study the soft tissues, the skin (if bandaged), the muscles and any organs retained in situ for magical or religious reason. Measurements of attenuation values indicate the materials which were used for mummifying the skin and organs. Characteristic examples are described and the early results of these examinations are discussed. (orig.) [de

  9. The traveling salesman problem a computational study

    CERN Document Server

    Applegate, David L; Chvatal, Vasek; Cook, William J

    2006-01-01

    This book presents the latest findings on one of the most intensely investigated subjects in computational mathematics--the traveling salesman problem. It sounds simple enough: given a set of cities and the cost of travel between each pair of them, the problem challenges you to find the cheapest route by which to visit all the cities and return home to where you began. Though seemingly modest, this exercise has inspired studies by mathematicians, chemists, and physicists. Teachers use it in the classroom. It has practical applications in genetics, telecommunications, and neuroscience.

  10. A New Approach to Tuning Heuristic Parameters of Genetic Algorithms

    Czech Academy of Sciences Publication Activity Database

    Holeňa, Martin

    2006-01-01

    Roč. 3, č. 3 (2006), s. 562-569 ISSN 1790-0832. [AIKED'06. WSEAS International Conference on Artificial Intelligence , Knowledge Engineering and Data Bases. Madrid, 15.02.2006-17.02.2006] R&D Projects: GA ČR(CZ) GA201/05/0325; GA ČR(CZ) GA201/05/0557 Institutional research plan: CEZ:AV0Z10300504 Keywords : evolutionary optimization * genetic algorithms * heuristic parameters * parameter tuning * artificial neural networks * convergence speed * population diversity Subject RIV: IN - Informatics, Computer Science

  11. A GPU-Based Genetic Algorithm for the P-Median Problem

    OpenAIRE

    AlBdaiwi, Bader F.; AboElFotoh, Hosam M. F.

    2016-01-01

    The p-median problem is a well-known NP-hard problem. Many heuristics have been proposed in the literature for this problem. In this paper, we exploit a GPGPU parallel computing platform to present a new genetic algorithm implemented in Cuda and based on a Pseudo Boolean formulation of the p-median problem. We have tested the effectiveness of our algorithm using a Tesla K40 (2880 Cuda cores) on 290 different benchmark instances obtained from OR-Library, discrete location problems benchmark li...

  12. Search for major genes with progeny test data to accelerate the development of genetically superior loblolly pine

    Energy Technology Data Exchange (ETDEWEB)

    NCSU

    2003-12-30

    This research project is to develop a novel approach that fully utilized the current breeding materials and genetic test information available from the NCSU-Industry Cooperative Tree Improvement Program to identify major genes that are segregating for growth and disease resistance in loblolly pine. If major genes can be identified in the existing breeding population, they can be utilized directly in the conventional loblolly pine breeding program. With the putative genotypes of parents identified, tree breeders can make effective decisions on management of breeding populations and operational deployment of genetically superior trees. Forest productivity will be significantly enhanced if genetically superior genotypes with major genes for economically important traits could be deployed in an operational plantation program. The overall objective of the project is to develop genetic model and analytical methods for major gene detection with progeny test data and accelerate the development of genetically superior loblolly pine. Specifically, there are three main tasks: (1) Develop genetic models for major gene detection and implement statistical methods and develop computer software for screening progeny test data; (2) Confirm major gene segregation with molecular markers; and (3) Develop strategies for using major genes for tree breeding.

  13. [Genetics factors in pathogenesis and clinical genetics of binge eating disorder].

    Science.gov (United States)

    Kibitov, А О; Мazo, G E

    2016-01-01

    Genetic studies have shown that binge eating disorder (ВЕD) aggregates in families, heritability was estimated as about 60% and additive genetic influences on BED up to 50%. Using a genetic approach has proved useful for verifying the diagnostic categories of BED using DSM-IV criteria and supporting the validity of considering this pathology as a separate nosological category. The results confirmed the genetic and pathogenic originality of BED as a separate psychopathological phenomenon, but not a subtype of obesity. It seems fruitful to considerate BED as a disease with hereditary predisposition with significant genetic influence and a complex psychopathological syndrome, including not only eating disorders, but also depressive and addictive component. A possible mechanism of pathogenesis of BED may be the interaction of the neuroendocrine and neurotransmitters systems including the active involvement of the reward system in response to a variety of chronic stress influences with the important modulatory role of specific personality traits. The high level of genetic influence on the certain clinical manifestations of BED confirms the ability to identify the subphenotypes of BED on genetic basis involving clinical criteria. It can not only contribute to further genetic studies, taking into account more homogeneous samples, but also help in finding differentiated therapeutic approaches.

  14. Determination of nonlinear genetic architecture using compressed sensing.

    Science.gov (United States)

    Ho, Chiu Man; Hsu, Stephen D H

    2015-01-01

    One of the fundamental problems of modern genomics is to extract the genetic architecture of a complex trait from a data set of individual genotypes and trait values. Establishing this important connection between genotype and phenotype is complicated by the large number of candidate genes, the potentially large number of causal loci, and the likely presence of some nonlinear interactions between different genes. Compressed Sensing methods obtain solutions to under-constrained systems of linear equations. These methods can be applied to the problem of determining the best model relating genotype to phenotype, and generally deliver better performance than simply regressing the phenotype against each genetic variant, one at a time. We introduce a Compressed Sensing method that can reconstruct nonlinear genetic models (i.e., including epistasis, or gene-gene interactions) from phenotype-genotype (GWAS) data. Our method uses L1-penalized regression applied to nonlinear functions of the sensing matrix. The computational and data resource requirements for our method are similar to those necessary for reconstruction of linear genetic models (or identification of gene-trait associations), assuming a condition of generalized sparsity, which limits the total number of gene-gene interactions. An example of a sparse nonlinear model is one in which a typical locus interacts with several or even many others, but only a small subset of all possible interactions exist. It seems plausible that most genetic architectures fall in this category. We give theoretical arguments suggesting that the method is nearly optimal in performance, and demonstrate its effectiveness on broad classes of nonlinear genetic models using simulated human genomes and the small amount of currently available real data. A phase transition (i.e., dramatic and qualitative change) in the behavior of the algorithm indicates when sufficient data is available for its successful application. Our results indicate

  15. Stocking the genetic supermarket: reproductive genetic technologies and collective action problems.

    Science.gov (United States)

    Gyngell, Chris; Douglas, Thomas

    2015-05-01

    Reproductive genetic technologies (RGTs) allow parents to decide whether their future children will have or lack certain genetic predispositions. A popular model that has been proposed for regulating access to RGTs is the 'genetic supermarket'. In the genetic supermarket, parents are free to make decisions about which genes to select for their children with little state interference. One possible consequence of the genetic supermarket is that collective action problems will arise: if rational individuals use the genetic supermarket in isolation from one another, this may have a negative effect on society as a whole, including future generations. In this article we argue that RGTs targeting height, innate immunity, and certain cognitive traits could lead to collective action problems. We then discuss whether this risk could in principle justify state intervention in the genetic supermarket. We argue that there is a plausible prima facie case for the view that such state intervention would be justified and respond to a number of arguments that might be adduced against that view. © 2014 The Authors. Bioethics published by John Wiley & Sons Ltd.

  16. Multi-objective hierarchical genetic algorithms for multilevel redundancy allocation optimization

    Energy Technology Data Exchange (ETDEWEB)

    Kumar, Ranjan [Department of Aeronautics and Astronautics, Kyoto University, Yoshida-honmachi, Sakyo-ku, Kyoto 606-8501 (Japan)], E-mail: ranjan.k@ks3.ecs.kyoto-u.ac.jp; Izui, Kazuhiro [Department of Aeronautics and Astronautics, Kyoto University, Yoshida-honmachi, Sakyo-ku, Kyoto 606-8501 (Japan)], E-mail: izui@prec.kyoto-u.ac.jp; Yoshimura, Masataka [Department of Aeronautics and Astronautics, Kyoto University, Yoshida-honmachi, Sakyo-ku, Kyoto 606-8501 (Japan)], E-mail: yoshimura@prec.kyoto-u.ac.jp; Nishiwaki, Shinji [Department of Aeronautics and Astronautics, Kyoto University, Yoshida-honmachi, Sakyo-ku, Kyoto 606-8501 (Japan)], E-mail: shinji@prec.kyoto-u.ac.jp

    2009-04-15

    Multilevel redundancy allocation optimization problems (MRAOPs) occur frequently when attempting to maximize the system reliability of a hierarchical system, and almost all complex engineering systems are hierarchical. Despite their practical significance, limited research has been done concerning the solving of simple MRAOPs. These problems are not only NP hard but also involve hierarchical design variables. Genetic algorithms (GAs) have been applied in solving MRAOPs, since they are computationally efficient in solving such problems, unlike exact methods, but their applications has been confined to single-objective formulation of MRAOPs. This paper proposes a multi-objective formulation of MRAOPs and a methodology for solving such problems. In this methodology, a hierarchical GA framework for multi-objective optimization is proposed by introducing hierarchical genotype encoding for design variables. In addition, we implement the proposed approach by integrating the hierarchical genotype encoding scheme with two popular multi-objective genetic algorithms (MOGAs)-the strength Pareto evolutionary genetic algorithm (SPEA2) and the non-dominated sorting genetic algorithm (NSGA-II). In the provided numerical examples, the proposed multi-objective hierarchical approach is applied to solve two hierarchical MRAOPs, a 4- and a 3-level problems. The proposed method is compared with a single-objective optimization method that uses a hierarchical genetic algorithm (HGA), also applied to solve the 3- and 4-level problems. The results show that a multi-objective hierarchical GA (MOHGA) that includes elitism and mechanism for diversity preserving performed better than a single-objective GA that only uses elitism, when solving large-scale MRAOPs. Additionally, the experimental results show that the proposed method with NSGA-II outperformed the proposed method with SPEA2 in finding useful Pareto optimal solution sets.

  17. Multi-objective hierarchical genetic algorithms for multilevel redundancy allocation optimization

    International Nuclear Information System (INIS)

    Kumar, Ranjan; Izui, Kazuhiro; Yoshimura, Masataka; Nishiwaki, Shinji

    2009-01-01

    Multilevel redundancy allocation optimization problems (MRAOPs) occur frequently when attempting to maximize the system reliability of a hierarchical system, and almost all complex engineering systems are hierarchical. Despite their practical significance, limited research has been done concerning the solving of simple MRAOPs. These problems are not only NP hard but also involve hierarchical design variables. Genetic algorithms (GAs) have been applied in solving MRAOPs, since they are computationally efficient in solving such problems, unlike exact methods, but their applications has been confined to single-objective formulation of MRAOPs. This paper proposes a multi-objective formulation of MRAOPs and a methodology for solving such problems. In this methodology, a hierarchical GA framework for multi-objective optimization is proposed by introducing hierarchical genotype encoding for design variables. In addition, we implement the proposed approach by integrating the hierarchical genotype encoding scheme with two popular multi-objective genetic algorithms (MOGAs)-the strength Pareto evolutionary genetic algorithm (SPEA2) and the non-dominated sorting genetic algorithm (NSGA-II). In the provided numerical examples, the proposed multi-objective hierarchical approach is applied to solve two hierarchical MRAOPs, a 4- and a 3-level problems. The proposed method is compared with a single-objective optimization method that uses a hierarchical genetic algorithm (HGA), also applied to solve the 3- and 4-level problems. The results show that a multi-objective hierarchical GA (MOHGA) that includes elitism and mechanism for diversity preserving performed better than a single-objective GA that only uses elitism, when solving large-scale MRAOPs. Additionally, the experimental results show that the proposed method with NSGA-II outperformed the proposed method with SPEA2 in finding useful Pareto optimal solution sets

  18. Detection of Genetically Modified Food: Has Your Food Been Genetically Modified?

    Science.gov (United States)

    Brandner, Diana L.

    2002-01-01

    Explains the benefits and risks of genetically-modified foods and describes methods for genetically modifying food. Presents a laboratory experiment using a polymerase chain reaction (PCR) test to detect foreign DNA in genetically-modified food. (Contains 18 references.) (YDS)

  19. Abstracts of the 48. Brazilian congress on genetics. Genetics in social inclusion

    International Nuclear Information System (INIS)

    2002-01-01

    Use of radioisotopes and ionizing radiations in genetics is presented. Several aspects related to men, animals, plants and microorganisms are reported highlighting biological radiation effects, evolution, mutagenesis and genetic engineering. Genetic mapping, polymerase chain reaction, gene mutations, genetic diversity, DNA hybridization, DNA sequencing, plant cultivation and plant grow are studied as well

  20. Genetic Testing and Its Implications: Human Genetics Researchers Grapple with Ethical Issues.

    Science.gov (United States)

    Rabino, Isaac

    2003-01-01

    Contributes systematic data on the attitudes of scientific experts who engage in human genetics research about the pros, cons, and ethical implications of genetic testing. Finds that they are highly supportive of voluntary testing and the right to know one's genetic heritage. Calls for greater genetic literacy. (Contains 87 references.) (Author/NB)

  1. Multilayer perceptron for robust nonlinear interval regression analysis using genetic algorithms.

    Science.gov (United States)

    Hu, Yi-Chung

    2014-01-01

    On the basis of fuzzy regression, computational models in intelligence such as neural networks have the capability to be applied to nonlinear interval regression analysis for dealing with uncertain and imprecise data. When training data are not contaminated by outliers, computational models perform well by including almost all given training data in the data interval. Nevertheless, since training data are often corrupted by outliers, robust learning algorithms employed to resist outliers for interval regression analysis have been an interesting area of research. Several approaches involving computational intelligence are effective for resisting outliers, but the required parameters for these approaches are related to whether the collected data contain outliers or not. Since it seems difficult to prespecify the degree of contamination beforehand, this paper uses multilayer perceptron to construct the robust nonlinear interval regression model using the genetic algorithm. Outliers beyond or beneath the data interval will impose slight effect on the determination of data interval. Simulation results demonstrate that the proposed method performs well for contaminated datasets.

  2. PRESAGE: PRivacy-preserving gEnetic testing via SoftwAre Guard Extension.

    Science.gov (United States)

    Chen, Feng; Wang, Chenghong; Dai, Wenrui; Jiang, Xiaoqian; Mohammed, Noman; Al Aziz, Md Momin; Sadat, Md Nazmus; Sahinalp, Cenk; Lauter, Kristin; Wang, Shuang

    2017-07-26

    Advances in DNA sequencing technologies have prompted a wide range of genomic applications to improve healthcare and facilitate biomedical research. However, privacy and security concerns have emerged as a challenge for utilizing cloud computing to handle sensitive genomic data. We present one of the first implementations of Software Guard Extension (SGX) based securely outsourced genetic testing framework, which leverages multiple cryptographic protocols and minimal perfect hash scheme to enable efficient and secure data storage and computation outsourcing. We compared the performance of the proposed PRESAGE framework with the state-of-the-art homomorphic encryption scheme, as well as the plaintext implementation. The experimental results demonstrated significant performance over the homomorphic encryption methods and a small computational overhead in comparison to plaintext implementation. The proposed PRESAGE provides an alternative solution for secure and efficient genomic data outsourcing in an untrusted cloud by using a hybrid framework that combines secure hardware and multiple crypto protocols.

  3. Computational Methods to Work as First-Pass Filter in Deleterious SNP Analysis of Alkaptonuria

    Directory of Open Access Journals (Sweden)

    R. Magesh

    2012-01-01

    Full Text Available A major challenge in the analysis of human genetic variation is to distinguish functional from nonfunctional SNPs. Discovering these functional SNPs is one of the main goals of modern genetics and genomics studies. There is a need to effectively and efficiently identify functionally important nsSNPs which may be deleterious or disease causing and to identify their molecular effects. The prediction of phenotype of nsSNPs by computational analysis may provide a good way to explore the function of nsSNPs and its relationship with susceptibility to disease. In this context, we surveyed and compared variation databases along with in silico prediction programs to assess the effects of deleterious functional variants on protein functions. In other respects, we attempted these methods to work as first-pass filter to identify the deleterious substitutions worth pursuing for further experimental research. In this analysis, we used the existing computational methods to explore the mutation-structure-function relationship in HGD gene causing alkaptonuria.

  4. Computational Methods to Work as First-Pass Filter in Deleterious SNP Analysis of Alkaptonuria

    Science.gov (United States)

    Magesh, R.; George Priya Doss, C.

    2012-01-01

    A major challenge in the analysis of human genetic variation is to distinguish functional from nonfunctional SNPs. Discovering these functional SNPs is one of the main goals of modern genetics and genomics studies. There is a need to effectively and efficiently identify functionally important nsSNPs which may be deleterious or disease causing and to identify their molecular effects. The prediction of phenotype of nsSNPs by computational analysis may provide a good way to explore the function of nsSNPs and its relationship with susceptibility to disease. In this context, we surveyed and compared variation databases along with in silico prediction programs to assess the effects of deleterious functional variants on protein functions. In other respects, we attempted these methods to work as first-pass filter to identify the deleterious substitutions worth pursuing for further experimental research. In this analysis, we used the existing computational methods to explore the mutation-structure-function relationship in HGD gene causing alkaptonuria. PMID:22606059

  5. Inference and Analysis of Population Structure Using Genetic Data and Network Theory.

    Science.gov (United States)

    Greenbaum, Gili; Templeton, Alan R; Bar-David, Shirli

    2016-04-01

    Clustering individuals to subpopulations based on genetic data has become commonplace in many genetic studies. Inference about population structure is most often done by applying model-based approaches, aided by visualization using distance-based approaches such as multidimensional scaling. While existing distance-based approaches suffer from a lack of statistical rigor, model-based approaches entail assumptions of prior conditions such as that the subpopulations are at Hardy-Weinberg equilibria. Here we present a distance-based approach for inference about population structure using genetic data by defining population structure using network theory terminology and methods. A network is constructed from a pairwise genetic-similarity matrix of all sampled individuals. The community partition, a partition of a network to dense subgraphs, is equated with population structure, a partition of the population to genetically related groups. Community-detection algorithms are used to partition the network into communities, interpreted as a partition of the population to subpopulations. The statistical significance of the structure can be estimated by using permutation tests to evaluate the significance of the partition's modularity, a network theory measure indicating the quality of community partitions. To further characterize population structure, a new measure of the strength of association (SA) for an individual to its assigned community is presented. The strength of association distribution (SAD) of the communities is analyzed to provide additional population structure characteristics, such as the relative amount of gene flow experienced by the different subpopulations and identification of hybrid individuals. Human genetic data and simulations are used to demonstrate the applicability of the analyses. The approach presented here provides a novel, computationally efficient model-free method for inference about population structure that does not entail assumption of

  6. Computational aspects of feedback in neural circuits.

    Directory of Open Access Journals (Sweden)

    Wolfgang Maass

    2007-01-01

    throw new light on the computational role of feedback in other complex biological dynamical systems, such as, for example, genetic regulatory networks.

  7. Development of a graphical interface computer code for reactor fuel reloading optimization

    International Nuclear Information System (INIS)

    Do Quang Binh; Nguyen Phuoc Lan; Bui Xuan Huy

    2007-01-01

    This report represents the results of the project performed in 2007. The aim of this project is to develop a graphical interface computer code that allows refueling engineers to design fuel reloading patterns for research reactor using simulated graphical model of reactor core. Besides, this code can perform refueling optimization calculations based on genetic algorithms as well as simulated annealing. The computer code was verified based on a sample problem, which relies on operational and experimental data of Dalat research reactor. This code can play a significant role in in-core fuel management practice at nuclear research reactor centers and in training. (author)

  8. On Gene Concepts and Teaching Genetics: Episodes from Classical Genetics

    Science.gov (United States)

    Burian, Richard M.

    2013-02-01

    This paper addresses the teaching of advanced high school courses or undergraduate courses for non-biology majors about genetics or history of genetics. It will probably be difficult to take the approach described here in a high school science course, although the general approach could help improve such courses. It would be ideal for a college course in history of genetics or a course designed to teach non-science majors how science works or the rudiments of the genetics in a way that will help them as citizens. The approach aims to teach the processes of discovery, correction, and validation by utilizing illustrative episodes from the history of genetics. The episodes are treated in way that should foster understanding of basic questions about genes, the sorts of techniques used to answer questions about the constitution and structure of genes, how they function, and what they determine, and some of the major biological disagreements that arose in dealing with these questions. The material covered here could be connected to social and political issues raised by genetics, but these connections are not surveyed here. As it is, to cover this much territory, the article is limited to four major episodes from Mendel's paper to the beginning of World War II. A sequel will deal with the molecularization of genetics and with molecular gene concepts through the Human Genome Project.

  9. Nonpher: computational method for design of hard-to-synthesize structures

    Czech Academy of Sciences Publication Activity Database

    Voršilák, M.; Svozil, Daniel

    2017-01-01

    Roč. 9, březen (2017), č. článku 20. ISSN 1758-2946 R&D Projects: GA MŠk LO1220; GA MŠk LM2015063 Institutional support: RVO:68378050 Keywords : synthetic feasibility * molecular complexity * molecular morphing Subject RIV: EB - Genetics ; Molecular Biology OBOR OECD: Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8) Impact factor: 4.220, year: 2016

  10. Research on non-uniform strain profile reconstruction along fiber Bragg grating via genetic programming algorithm and interrelated experimental verification

    Science.gov (United States)

    Zheng, Shijie; Zhang, Nan; Xia, Yanjun; Wang, Hongtao

    2014-03-01

    A new heuristic strategy for the non-uniform strain profile reconstruction along Fiber Bragg Gratings is proposed in this paper, which is based on the modified transfer matrix and Genetic Programming(GP) algorithm. The present method uses Genetic Programming to determine the applied strain field as a function of position along the fiber length. The structures that undergo adaptation in genetic programming are hierarchical structures which are different from that of conventional genetic algorithm operating on strings. GP regress the strain profile function which matches the 'measured' spectrum best and makes space resolution of strain reconstruction arbitrarily high, or even infinite. This paper also presents an experimental verification of the reconstruction of non-homogeneous strain fields using GP. The results are compared with numerical calculations of finite element method. Both the simulation examples and experimental results demonstrate that Genetic Programming can effectively reconstruct continuous profile expression along the whole FBG, and greatly improves its computational efficiency and accuracy.

  11. Genetics education for non-genetic health care professionals in the Netherlands

    NARCIS (Netherlands)

    Plass, Anne Marie C.; Baars, Marieke J. H.; Beemer, Frits A.; ten Kate, Leo P.

    2006-01-01

    OBJECTIVE: The aim of the present study was to investigate whether medical care providers in the Netherlands are adequately educated in genetics by collecting information about the current state of genetics education of non-genetics health care professionals. METHOD: The curricula of the 8

  12. Reassessing insurers' access to genetic information: genetic privacy, ignorance, and injustice.

    Science.gov (United States)

    Feiring, Eli

    2009-06-01

    Many countries have imposed strict regulations on the genetic information to which insurers have access. Commentators have warned against the emerging body of legislation for different reasons. This paper demonstrates that, when confronted with the argument that genetic information should be available to insurers for health insurance underwriting purposes, one should avoid appeals to rights of genetic privacy and genetic ignorance. The principle of equality of opportunity may nevertheless warrant restrictions. A choice-based account of this principle implies that it is unfair to hold people responsible for the consequences of the genetic lottery, since we have no choice in selecting our genotype or the expression of it. However appealing, this view does not take us all the way to an adequate justification of inaccessibility of genetic information. A contractarian account, suggesting that health is a condition of opportunity and that healthcare is an essential good, seems more promising. I conclude that if or when predictive medical tests (such as genetic tests) are developed with significant actuarial value, individuals have less reason to accept as fair institutions that limit access to healthcare on the grounds of risk status. Given the assumption that a division of risk pools in accordance with a rough estimate of people's level of (genetic) risk will occur, fairness and justice favour universal health insurance based on solidarity.

  13. Patterns of ancestry and genetic diversity in reintroduced populations of the slimy sculpin: Implications for conservation

    Science.gov (United States)

    Huff, David D.; Miller, Loren M.; Vondracek, Bruce C.

    2010-01-01

    Reintroductions are a common approach for preserving intraspecific biodiversity in fragmented landscapes. However, they may exacerbate the reduction in genetic diversity initially caused by population fragmentation because the effective population size of reintroduced populations is often smaller and reintroduced populations also tend to be more geographically isolated than native populations. Mixing genetically divergent sources for reintroduction purposes is a practice intended to increase genetic diversity. We documented the outcome of reintroductions from three mixed sources on the ancestral composition and genetic variation of a North American fish, the slimy sculpin (Cottus cognatus). We used microsatellite markers to evaluate allelic richness and heterozygosity in the reintroduced populations relative to computer simulated expectations. Sculpins in reintroduced populations exhibited higher levels of heterozygosity and allelic richness than any single source, but only slightly higher than the single most genetically diverse source population. Simulations intended to mimic an ideal scenario for maximizing genetic variation in the reintroduced populations also predicted increases, but they were only moderately greater than the most variable source population. We found that a single source contributed more than the other two sources at most reintroduction sites. We urge caution when choosing whether to mix source populations in reintroduction programs. Genetic characteristics of candidate source populations should be evaluated prior to reintroduction if feasible. When combined with knowledge of the degree of genetic distinction among sources, simulations may allow the genetic diversity benefits of mixing populations to be weighed against the risks of outbreeding depression in reintroduced and nearby populations.

  14. Shape: automatic conformation prediction of carbohydrates using a genetic algorithm

    Directory of Open Access Journals (Sweden)

    Rosen Jimmy

    2009-09-01

    Full Text Available Abstract Background Detailed experimental three dimensional structures of carbohydrates are often difficult to acquire. Molecular modelling and computational conformation prediction are therefore commonly used tools for three dimensional structure studies. Modelling procedures generally require significant training and computing resources, which is often impractical for most experimental chemists and biologists. Shape has been developed to improve the availability of modelling in this field. Results The Shape software package has been developed for simplicity of use and conformation prediction performance. A trivial user interface coupled to an efficient genetic algorithm conformation search makes it a powerful tool for automated modelling. Carbohydrates up to a few hundred atoms in size can be investigated on common computer hardware. It has been shown to perform well for the prediction of over four hundred bioactive oligosaccharides, as well as compare favourably with previously published studies on carbohydrate conformation prediction. Conclusion The Shape fully automated conformation prediction can be used by scientists who lack significant modelling training, and performs well on computing hardware such as laptops and desktops. It can also be deployed on computer clusters for increased capacity. The prediction accuracy under the default settings is good, as it agrees well with experimental data and previously published conformation prediction studies. This software is available both as open source and under commercial licenses.

  15. Basic concepts of medical genetics, formal genetics, Part 1

    African Journals Online (AJOL)

    Mohammad Saad Zaghloul Salem

    2013-11-15

    Nov 15, 2013 ... maps of gene loci based on information gathered, formerly, ... represented as figure or text interface data. Relevant ... The Egyptian Journal of Medical Human Genetics ... prophylactic management and genetic counseling. 17.

  16. SERS-based detection methods for screening of genetically modified bacterial strains

    DEFF Research Database (Denmark)

    Morelli, Lidia

    factories vary largely, including industrial production of valuable compounds for biofuels, polymer synthesis and food, cosmetic and pharmaceutical industry. The improvement of computational and biochemical tools has revolutionized the synthesis of novel modified microbial strains, opening up new......The importance of metabolic engineering has been growing over the last decades, establishing the use of genetically modified microbial strains for overproduction of metabolites at industrial scale as an innovative, convenient and biosustainable method. Nowadays, application areas of microbial...

  17. Genetic secrets: Protecting privacy and confidentiality in the genetic era. Final report

    Energy Technology Data Exchange (ETDEWEB)

    Rothstein, M.A. [ed.

    1998-09-01

    Few developments are likely to affect human beings more profoundly in the long run than the discoveries resulting from advances in modern genetics. Although the developments in genetic technology promise to provide many additional benefits, their application to genetic screening poses ethical, social, and legal questions, many of which are rooted in issues of privacy and confidentiality. The ethical, practical, and legal ramifications of these and related questions are explored in depth. The broad range of topics includes: the privacy and confidentiality of genetic information; the challenges to privacy and confidentiality that may be projected to result from the emerging genetic technologies; the role of informed consent in protecting the confidentiality of genetic information in the clinical setting; the potential uses of genetic information by third parties; the implications of changes in the health care delivery system for privacy and confidentiality; relevant national and international developments in public policies, professional standards, and laws; recommendations; and the identification of research needs.

  18. The multivariate Dirichlet-multinomial distribution and its application in forensic genetics to adjust for subpopulation effects using the θ-correction

    DEFF Research Database (Denmark)

    Tvedebrink, Torben; Eriksen, Poul Svante; Morling, Niels

    2015-01-01

    In this paper, we discuss the construction of a multivariate generalisation of the Dirichlet-multinomial distribution. An example from forensic genetics in the statistical analysis of DNA mixtures motivates the study of this multivariate extension. In forensic genetics, adjustment of the match...... probabilities due to remote ancestry in the population is often done using the so-called θ-correction. This correction increases the probability of observing multiple copies of rare alleles in a subpopulation and thereby reduces the weight of the evidence for rare genotypes. A recent publication by Cowell et al....... (2015) showed elegantly how to use Bayesian networks for efficient computations of likelihood ratios in a forensic genetic context. However, their underlying population genetic model assumed independence of alleles, which is not realistic in real populations. We demonstrate how the so-called θ...

  19. Radiation induced mutants in elite genetic background for the augmentation of genetic diversity

    International Nuclear Information System (INIS)

    Kumar, V.; Bhagwat, S.G.

    2011-01-01

    Rice (Oryza sativa L.), an important food crop for India, shows large genetic diversity. However, despite the large genetic resource, high genetic similarity is reported in cultivated varieties indicating genetic erosion. Radiation induced mutations provide genetic variability in elite background. In the present study, twenty gamma ray induced mutants of rice variety WL112 (carrying sd-1 semi-dwarfing gene) were analysed for genetic diversity using microsatellite markers. The high range of genetic diversity among mutants indicated that the mutants possess potential for enhancing variability in rice. Cluster analysis showed presence of five clusters having small sub-clusters. Earliness, semi-dwarf stature or resistance to blast disease observed among the mutants showed that these will be useful in breeding programmes. (author)

  20. Enhanced computational methods for quantifying the effect of geographic and environmental isolation on genetic differentiation

    DEFF Research Database (Denmark)

    Botta, Filippo; Eriksen, Casper; Fontaine, Michaël C.

    2015-01-01

    1. In a recent paper, Bradburd et al. (Evolution, 67, 2013, 3258) proposed a model to quantify the relative effect of geographic and environmental distance on genetic differentiation. Here, we enhance this method in several ways. 2. We modify the covariance model so as to fit better with mainstre...... available as an R package called sunder. It takes as input georeferenced allele counts at the individual or population level for co-dominant markers. Program homepage: http://www2.imm.dtu.dk/~gigu/Sunder/....

  1. Genetics of nonsyndromic obesity.

    Science.gov (United States)

    Lee, Yung Seng

    2013-12-01

    Common obesity is widely regarded as a complex, multifactorial trait influenced by the 'obesogenic' environment, sedentary behavior, and genetic susceptibility contributed by common and rare genetic variants. This review describes the recent advances in understanding the role of genetics in obesity. New susceptibility loci and genetic variants are being uncovered, but the collective effect is relatively small and could not explain most of the BMI heritability. Yet-to-be identified common and rare variants, epistasis, and heritable epigenetic changes may account for part of the 'missing heritability'. Evidence is emerging about the role of epigenetics in determining obesity susceptibility, mediating developmental plasticity, which confers obesity risk from early life experiences. Genetic prediction scores derived from selected genetic variants, and also differential DNA methylation levels and methylation scores, have been shown to correlate with measures of obesity and response to weight loss intervention. Genetic variants, which confer susceptibility to obesity-related morbidities like nonalcoholic fatty liver disease, were also discovered recently. We can expect discovery of more rare genetic variants with the advent of whole exome and genome sequencing, and also greater understanding of epigenetic mechanisms by which environment influences genetic expression and which mediate the gene-environment interaction.

  2. What Is Genetic Ancestry Testing?

    Science.gov (United States)

    ... What is genetic ancestry testing? What is genetic ancestry testing? Genetic ancestry testing, or genetic genealogy, is ... with other groups. For more information about genetic ancestry testing: The University of Utah provides video tutorials ...

  3. Genetic aspects of pathological gambling: a complex disorder with shared genetic vulnerabilities.

    Science.gov (United States)

    Lobo, Daniela S S; Kennedy, James L

    2009-09-01

    To summarize and discuss findings from genetic studies conducted on pathological gambling (PG). Searches were conducted on PubMed and PsychInfo databases using the keywords: 'gambling and genes', 'gambling and family' and 'gambling and genetics', yielding 18 original research articles investigating the genetics of PG. Twin studies using the Vietnam Era Twin Registry have found that: (i) the heritability of PG is estimated to be 50-60%; (ii) PG and subclinical PG are a continuum of the same disorder; (iii) PG shares genetic vulnerability factors with antisocial behaviours, alcohol dependence and major depressive disorder; (iv) genetic factors underlie the association between exposure to traumatic life-events and PG. Molecular genetic investigations on PG are at an early stage and published studies have reported associations with genes involved in the brain's reward and impulse control systems. Despite the paucity of studies in this area, published studies have provided considerable evidence of the influence of genetic factors on PG and its complex interaction with other psychiatric disorders and environmental factors. The next step would be to investigate the association and interaction of these variables in larger molecular genetic studies with subphenotypes that underlie PG. Results from family and genetic investigations corroborate further the importance of understanding the biological underpinnings of PG in the development of more specific treatment and prevention strategies.

  4. Soft and hard computing approaches for real-time prediction of currents in a tide-dominated coastal area

    Digital Repository Service at National Institute of Oceanography (India)

    Charhate, S.B.; Deo, M.C.; SanilKumar, V.

    . Owing to the complex real sea conditions, such methods may not always yield satisfactory results. This paper discusses a few alternative approaches based on the soft computing tools of artificial neural networks (ANNs) and genetic programming (GP...

  5. Cognitive Development, Genetics Problem Solving, and Genetics Instruction: A Critical Review.

    Science.gov (United States)

    Smith, Mike U.; Sims, O. Suthern, Jr.

    1992-01-01

    Review of literature concerning problem solving in genetics and Piagetian stage theory. Authors conclude the research suggests that formal-operational thought is not strictly required for the solution of the majority of classical genetics problems; however, some genetic concepts are difficult for concrete operational students to understand.…

  6. Rough – Granular Computing knowledge discovery models

    Directory of Open Access Journals (Sweden)

    Mohammed M. Eissa

    2016-11-01

    Full Text Available Medical domain has become one of the most important areas of research in order to richness huge amounts of medical information about the symptoms of diseases and how to distinguish between them to diagnose it correctly. Knowledge discovery models play vital role in refinement and mining of medical indicators to help medical experts to settle treatment decisions. This paper introduces four hybrid Rough – Granular Computing knowledge discovery models based on Rough Sets Theory, Artificial Neural Networks, Genetic Algorithm and Rough Mereology Theory. A comparative analysis of various knowledge discovery models that use different knowledge discovery techniques for data pre-processing, reduction, and data mining supports medical experts to extract the main medical indicators, to reduce the misdiagnosis rates and to improve decision-making for medical diagnosis and treatment. The proposed models utilized two medical datasets: Coronary Heart Disease dataset and Hepatitis C Virus dataset. The main purpose of this paper was to explore and evaluate the proposed models based on Granular Computing methodology for knowledge extraction according to different evaluation criteria for classification of medical datasets. Another purpose is to make enhancement in the frame of KDD processes for supervised learning using Granular Computing methodology.

  7. Aortic root dimensions are predominantly determined by genetic factors: a classical twin study

    International Nuclear Information System (INIS)

    Celeng, Csilla; Kolossvary, Marton; Kovacs, Attila; Molnar, Andrea Agnes; Szilveszter, Balint; Karolyi, Mihaly; Jermendy, Adam L.; Karady, Julia; Merkely, Bela; Maurovich-Horvat, Pal; Horvath, Tamas; Tarnoki, Adam D.; Tarnoki, David L.; Voros, Szilard; Jermendy, Gyoergy

    2017-01-01

    Previous studies using transthoracic echocardiography (TTE) observed moderate heritability of aortic root dimensions. Computed tomography angiography (CTA) might provide more accurate heritability estimates. Our primary aim was to assess the heritability of the aortic root with CTA. Our secondary aim was to derive TTE-based heritability and compare this with the CTA-based results. In the BUDAPEST-GLOBAL study 198 twin subjects (118 monozygotic, 80 dizygotic; age 56.1 ± 9.4 years; 126 female) underwent CTA and TTE. We assessed the diameter of the left ventricular outflow tract (LVOT), annulus, sinus of Valsalva, sinotubular junction and ascending aorta. Heritability was assessed using ACDE model (A additive genetic, C common environmental, D dominant genetic, E unique environmental factors). Based on CTA, additive genetic effects were dominant (LVOT: A = 0.67, E = 0.33; annulus: A = 0.76, E = 0.24; sinus of Valsalva: A = 0.83, E = 0.17; sinotubular junction: A = 0.82, E = 0.18; ascending aorta: A = 0.75, E = 0.25). TTE-derived measurements showed moderate to no genetic influence (LVOT: A = 0.38, E = 0.62; annulus: C = 0.47, E = 0.53; sinus of Valsalva: C = 0.63, E = 0.37; sinotubular junction: C = 0.45, E = 0.55; ascending aorta: A = 0.67, E = 0.33). CTA-based assessment suggests that aortic root dimensions are predominantly determined by genetic factors. TTE-based measurements showed moderate to no genetic influence. The choice of measurement method has substantial impact on heritability estimates. (orig.)

  8. Aortic root dimensions are predominantly determined by genetic factors: a classical twin study

    Energy Technology Data Exchange (ETDEWEB)

    Celeng, Csilla; Kolossvary, Marton; Kovacs, Attila; Molnar, Andrea Agnes; Szilveszter, Balint; Karolyi, Mihaly; Jermendy, Adam L.; Karady, Julia; Merkely, Bela; Maurovich-Horvat, Pal [Semmelweis University, MTA-SE Cardiovascular Imaging Research Group, Heart and Vascular Center, Budapest (Hungary); Horvath, Tamas [Budapest University of Technology and Economics, Department of Hydrodynamic Systems, Budapest (Hungary); Tarnoki, Adam D.; Tarnoki, David L. [Semmelweis University, Department of Radiology and Oncotherapy, Budapest (Hungary); Voros, Szilard [Global Genomics Group, Atlanta, GA (United States); Jermendy, Gyoergy [Bajcsy-Zsilinszky Hospital, Medical Department, Budapest (Hungary)

    2017-06-15

    Previous studies using transthoracic echocardiography (TTE) observed moderate heritability of aortic root dimensions. Computed tomography angiography (CTA) might provide more accurate heritability estimates. Our primary aim was to assess the heritability of the aortic root with CTA. Our secondary aim was to derive TTE-based heritability and compare this with the CTA-based results. In the BUDAPEST-GLOBAL study 198 twin subjects (118 monozygotic, 80 dizygotic; age 56.1 ± 9.4 years; 126 female) underwent CTA and TTE. We assessed the diameter of the left ventricular outflow tract (LVOT), annulus, sinus of Valsalva, sinotubular junction and ascending aorta. Heritability was assessed using ACDE model (A additive genetic, C common environmental, D dominant genetic, E unique environmental factors). Based on CTA, additive genetic effects were dominant (LVOT: A = 0.67, E = 0.33; annulus: A = 0.76, E = 0.24; sinus of Valsalva: A = 0.83, E = 0.17; sinotubular junction: A = 0.82, E = 0.18; ascending aorta: A = 0.75, E = 0.25). TTE-derived measurements showed moderate to no genetic influence (LVOT: A = 0.38, E = 0.62; annulus: C = 0.47, E = 0.53; sinus of Valsalva: C = 0.63, E = 0.37; sinotubular junction: C = 0.45, E = 0.55; ascending aorta: A = 0.67, E = 0.33). CTA-based assessment suggests that aortic root dimensions are predominantly determined by genetic factors. TTE-based measurements showed moderate to no genetic influence. The choice of measurement method has substantial impact on heritability estimates. (orig.)

  9. The Virtual Genetics Lab II: Improvements to a Freely Available Software Simulation of Genetics

    Science.gov (United States)

    White, Brian T.

    2012-01-01

    The Virtual Genetics Lab II (VGLII) is an improved version of the highly successful genetics simulation software, the Virtual Genetics Lab (VGL). The software allows students to use the techniques of genetic analysis to design crosses and interpret data to solve realistic genetics problems involving a hypothetical diploid insect. This is a brief…

  10. Genetic testing in the epilepsies—Report of the ILAE Genetics Commission

    Science.gov (United States)

    Ottman, Ruth; Hirose, Shinichi; Jain, Satish; Lerche, Holger; Lopes-Cendes, Iscia; Noebels, Jeffrey L.; Serratosa, José; Zara, Federico; Scheffer, Ingrid E.

    2010-01-01

    SUMMARY In this report, the International League Against Epilepsy (ILAE) Genetics Commission discusses essential issues to be considered with regard to clinical genetic testing in the epilepsies. Genetic research on the epilepsies has led to the identification of more than 20 genes with a major effect on susceptibility to idiopathic epilepsies. The most important potential clinical application of these discoveries is genetic testing: the use of genetic information, either to clarify the diagnosis in people already known or suspected to have epilepsy (diagnostic testing), or to predict onset of epilepsy in people at risk because of a family history (predictive testing). Although genetic testing has many potential benefits, it also has potential harms, and assessment of these potential benefits and harms in particular situations is complex. Moreover, many treating clinicians are unfamiliar with the types of tests available, how to access them, how to decide whether they should be offered, and what measures should be used to maximize benefit and minimize harm to their patients. Because the field is moving rapidly, with new information emerging practically every day, we present a framework for considering the clinical utility of genetic testing that can be applied to many different syndromes and clinical contexts. Given the current state of knowledge, genetic testing has high0020clinical utility in few clinical contexts, but in some of these it carries implications for daily clinical practice. PMID:20100225

  11. Genetic parameters and estimated genetic gains in young rubber tree progenies

    Directory of Open Access Journals (Sweden)

    Cecília Khusala Verardi

    2013-04-01

    Full Text Available The objective of this work was to assess the genetic parameters and to estimate genetic gains in young rubber tree progenies. The experiments were carried out during three years, in a randomized block design, with six replicates and ten plants per plot, in three representative Hevea crop regions of the state of São Paulo, Brazil. Twenty-two progenies were evaluated, from three to five years old, for rubber yield and annual girth growth. Genetic gain was estimated with the multi-effect index (MEI. Selection by progenies means provided greater estimated genetic gain than selection based on individuals, since heritability values of progeny means were greater than the ones of individual heritability, for both evaluated variables, in all the assessment years. The selection of the three best progenies for rubber yield provided a selection gain of 1.28 g per plant. The genetic gains estimated with MEI using data from early assessments (from 3 to 5-year-old were generally high for annual girth growth and rubber yield. The high genetic gains for annual girth growth in the first year of assessment indicate that progenies can be selected at the beginning of the breeding program. Population effective size was consistent with the three progenies selected, showing that they were not related and that the population genetic variability is ensured. Early selection with the genetic gains estimated by MEI can be made on rubber tree progenies.

  12. Genetic algorithm based on qubits and quantum gates

    International Nuclear Information System (INIS)

    Silva, Joao Batista Rosa; Ramos, Rubens Viana

    2003-01-01

    Full text: Genetic algorithm, a computational technique based on the evolution of the species, in which a possible solution of the problem is coded in a binary string, called chromosome, has been used successfully in several kinds of problems, where the search of a minimal or a maximal value is necessary, even when local minima are present. A natural generalization of a binary string is a qubit string. Hence, it is possible to use the structure of a genetic algorithm having a sequence of qubits as a chromosome and using quantum operations in the reproduction in order to find the best solution in some problems of quantum information. For example, given a unitary matrix U what is the pair of qubits that, when applied at the input, provides the output state with maximal entanglement? In order to solve this problem, a population of chromosomes of two qubits was created. The crossover was performed applying the quantum gates CNOT and SWAP at the pair of qubits, while the mutation was performed applying the quantum gates Hadamard, Z and Not in a single qubit. The result was compared with a classical genetic algorithm used to solve the same problem. A hundred simulations using the same U matrix was performed. Both algorithms, hereafter named by CGA (classical) and QGA (using qu bits), reached good results close to 1 however, the number of generations needed to find the best result was lower for the QGA. Another problem where the QGA can be useful is in the calculation of the relative entropy of entanglement. We have tested our algorithm using 100 pure states chosen randomly. The stop criterion used was the error lower than 0.01. The main advantages of QGA are its good precision, robustness and very easy implementation. The main disadvantage is its low velocity, as happen for all kind of genetic algorithms. (author)

  13. Molecular genetics made simple

    Directory of Open Access Journals (Sweden)

    Heba Sh. Kassem

    2012-07-01

    Full Text Available Genetics have undoubtedly become an integral part of biomedical science and clinical practice, with important implications in deciphering disease pathogenesis and progression, identifying diagnostic and prognostic markers, as well as designing better targeted treatments. The exponential growth of our understanding of different genetic concepts is paralleled by a growing list of genetic terminology that can easily intimidate the unfamiliar reader. Rendering genetics incomprehensible to the clinician however, defeats the very essence of genetic research: its utilization for combating disease and improving quality of life. Herein we attempt to correct this notion by presenting the basic genetic concepts along with their usefulness in the cardiology clinic. Bringing genetics closer to the clinician will enable its harmonious incorporation into clinical care, thus not only restoring our perception of its simple and elegant nature, but importantly ensuring the maximal benefit for our patients.

  14. Molecular genetics made simple

    Science.gov (United States)

    Kassem, Heba Sh.; Girolami, Francesca; Sanoudou, Despina

    2012-01-01

    Abstract Genetics have undoubtedly become an integral part of biomedical science and clinical practice, with important implications in deciphering disease pathogenesis and progression, identifying diagnostic and prognostic markers, as well as designing better targeted treatments. The exponential growth of our understanding of different genetic concepts is paralleled by a growing list of genetic terminology that can easily intimidate the unfamiliar reader. Rendering genetics incomprehensible to the clinician however, defeats the very essence of genetic research: its utilization for combating disease and improving quality of life. Herein we attempt to correct this notion by presenting the basic genetic concepts along with their usefulness in the cardiology clinic. Bringing genetics closer to the clinician will enable its harmonious incorporation into clinical care, thus not only restoring our perception of its simple and elegant nature, but importantly ensuring the maximal benefit for our patients. PMID:25610837

  15. Integrative Functional Genomics for Systems Genetics in GeneWeaver.org.

    Science.gov (United States)

    Bubier, Jason A; Langston, Michael A; Baker, Erich J; Chesler, Elissa J

    2017-01-01

    The abundance of existing functional genomics studies permits an integrative approach to interpreting and resolving the results of diverse systems genetics studies. However, a major challenge lies in assembling and harmonizing heterogeneous data sets across species for facile comparison to the positional candidate genes and coexpression networks that come from systems genetic studies. GeneWeaver is an online database and suite of tools at www.geneweaver.org that allows for fast aggregation and analysis of gene set-centric data. GeneWeaver contains curated experimental data together with resource-level data such as GO annotations, MP annotations, and KEGG pathways, along with persistent stores of user entered data sets. These can be entered directly into GeneWeaver or transferred from widely used resources such as GeneNetwork.org. Data are analyzed using statistical tools and advanced graph algorithms to discover new relations, prioritize candidate genes, and generate function hypotheses. Here we use GeneWeaver to find genes common to multiple gene sets, prioritize candidate genes from a quantitative trait locus, and characterize a set of differentially expressed genes. Coupling a large multispecies repository curated and empirical functional genomics data to fast computational tools allows for the rapid integrative analysis of heterogeneous data for interpreting and extrapolating systems genetics results.

  16. A Study on Soft Computing Applications in I and C Systems of Nuclear Power Plant

    International Nuclear Information System (INIS)

    Kang, H. T.; Chung, H. Y.

    2006-01-01

    In the paper, the application of the soft computing based nuclear power plant(NPP) is discussed. Soft computing such as neural network(NN), fuzzy logic controller(FLC), and genetic algorithm(GA) and/or their hybrid will be a new frontier for the development of instrument and control(I and C) systems in NPP. The application includes several fields, for example, the diagnostics of system transient, optimal data selection in NN, and intelligent control etc. Two or more combining structure, hybrid system, is more efficient. The concept of FLC, NN, and GA is presented in Section 2. The applications of soft computing used in NPP are presented in Section 3

  17. Genetic Resources in the “Calabaza Pipiana” Squash (Cucurbita argyrosperma) in Mexico: Genetic Diversity, Genetic Differentiation and Distribution Models

    Science.gov (United States)

    Sánchez-de la Vega, Guillermo; Castellanos-Morales, Gabriela; Gámez, Niza; Hernández-Rosales, Helena S.; Vázquez-Lobo, Alejandra; Aguirre-Planter, Erika; Jaramillo-Correa, Juan P.; Montes-Hernández, Salvador; Lira-Saade, Rafael; Eguiarte, Luis E.

    2018-01-01

    Analyses of genetic variation allow understanding the origin, diversification and genetic resources of cultivated plants. Domesticated taxa and their wild relatives are ideal systems for studying genetic processes of plant domestication and their joint is important to evaluate the distribution of their genetic resources. Such is the case of the domesticated subspecies C. argyrosperma ssp. argyrosperma, known in Mexico as calabaza pipiana, and its wild relative C. argyrosperma ssp. sororia. The main aim of this study was to use molecular data (microsatellites) to assess the levels of genetic variation and genetic differentiation within and among populations of domesticated argyrosperma across its distribution in Mexico in comparison to its wild relative, sororia, and to identify environmental suitability in previously proposed centers of domestication. We analyzed nine unlinked nuclear microsatellite loci to assess levels of diversity and distribution of genetic variation within and among populations in 440 individuals from 19 populations of cultivated landraces of argyrosperma and from six wild populations of sororia, in order to conduct a first systematic analysis of their genetic resources. We also used species distribution models (SDMs) for sororia to identify changes in this wild subspecies’ distribution from the Holocene (∼6,000 years ago) to the present, and to assess the presence of suitable environmental conditions in previously proposed domestication sites. Genetic variation was similar among subspecies (HE = 0.428 in sororia, and HE = 0.410 in argyrosperma). Nine argyrosperma populations showed significant levels of inbreeding. Both subspecies are well differentiated, and genetic differentiation (FST) among populations within each subspecies ranged from 0.152 to 0.652. Within argyrosperma we found three genetic groups (Northern Mexico, Yucatan Peninsula, including Michoacan and Veracruz, and Pacific coast plus Durango). We detected low levels of gene

  18. Genetic Resources in the “Calabaza Pipiana” Squash (Cucurbita argyrosperma in Mexico: Genetic Diversity, Genetic Differentiation and Distribution Models

    Directory of Open Access Journals (Sweden)

    Guillermo Sánchez-de la Vega

    2018-03-01

    Full Text Available Analyses of genetic variation allow understanding the origin, diversification and genetic resources of cultivated plants. Domesticated taxa and their wild relatives are ideal systems for studying genetic processes of plant domestication and their joint is important to evaluate the distribution of their genetic resources. Such is the case of the domesticated subspecies C. argyrosperma ssp. argyrosperma, known in Mexico as calabaza pipiana, and its wild relative C. argyrosperma ssp. sororia. The main aim of this study was to use molecular data (microsatellites to assess the levels of genetic variation and genetic differentiation within and among populations of domesticated argyrosperma across its distribution in Mexico in comparison to its wild relative, sororia, and to identify environmental suitability in previously proposed centers of domestication. We analyzed nine unlinked nuclear microsatellite loci to assess levels of diversity and distribution of genetic variation within and among populations in 440 individuals from 19 populations of cultivated landraces of argyrosperma and from six wild populations of sororia, in order to conduct a first systematic analysis of their genetic resources. We also used species distribution models (SDMs for sororia to identify changes in this wild subspecies’ distribution from the Holocene (∼6,000 years ago to the present, and to assess the presence of suitable environmental conditions in previously proposed domestication sites. Genetic variation was similar among subspecies (HE = 0.428 in sororia, and HE = 0.410 in argyrosperma. Nine argyrosperma populations showed significant levels of inbreeding. Both subspecies are well differentiated, and genetic differentiation (FST among populations within each subspecies ranged from 0.152 to 0.652. Within argyrosperma we found three genetic groups (Northern Mexico, Yucatan Peninsula, including Michoacan and Veracruz, and Pacific coast plus Durango. We detected low

  19. Genetic Romanticism

    DEFF Research Database (Denmark)

    Tupasela, Aaro

    2016-01-01

    inheritance as a way to unify populations within politically and geographically bounded areas. Thus, new genetics have contributed to the development of genetic romanticisms, whereby populations (human, plant, and animal) can be delineated and mobilized through scientific and medical practices to represent...

  20. kWIP: The k-mer weighted inner product, a de novo estimator of genetic similarity.

    Science.gov (United States)

    Murray, Kevin D; Webers, Christfried; Ong, Cheng Soon; Borevitz, Justin; Warthmann, Norman

    2017-09-01

    Modern genomics techniques generate overwhelming quantities of data. Extracting population genetic variation demands computationally efficient methods to determine genetic relatedness between individuals (or "samples") in an unbiased manner, preferably de novo. Rapid estimation of genetic relatedness directly from sequencing data has the potential to overcome reference genome bias, and to verify that individuals belong to the correct genetic lineage before conclusions are drawn using mislabelled, or misidentified samples. We present the k-mer Weighted Inner Product (kWIP), an assembly-, and alignment-free estimator of genetic similarity. kWIP combines a probabilistic data structure with a novel metric, the weighted inner product (WIP), to efficiently calculate pairwise similarity between sequencing runs from their k-mer counts. It produces a distance matrix, which can then be further analysed and visualised. Our method does not require prior knowledge of the underlying genomes and applications include establishing sample identity and detecting mix-up, non-obvious genomic variation, and population structure. We show that kWIP can reconstruct the true relatedness between samples from simulated populations. By re-analysing several published datasets we show that our results are consistent with marker-based analyses. kWIP is written in C++, licensed under the GNU GPL, and is available from https://github.com/kdmurray91/kwip.

  1. Genetic structure of farmer-managed varieties in clonally-propagated crops.

    Science.gov (United States)

    Scarcelli, N; Tostain, S; Vigouroux, Y; Luong, V; Baco, M N; Agbangla, C; Daïnou, O; Pham, J L

    2011-08-01

    The relative role of sexual reproduction and mutation in shaping the diversity of clonally propagated crops is largely unknown. We analyzed the genetic diversity of yam-a vegetatively-propagated crop-to gain insight into how these two factors shape its diversity in relation with farmers' classifications. Using 15 microsatellite loci, we analyzed 485 samples of 10 different yam varieties. We identified 33 different genotypes organized in lineages supported by high bootstrap values. We computed the probability that these genotypes appeared by sexual reproduction or mutation within and between each lineage. This allowed us to interpret each lineage as a product of sexual reproduction that has evolved by mutation. Moreover, we clearly noted a similarity between the genetic structure and farmers' classifications. Each variety could thus be interpreted as being the product of sexual reproduction having evolved by mutation. This highly structured diversity of farmer-managed varieties has consequences for the preservation of yam diversity.

  2. An Enhanced Genetic Algorithm for the Generalized Traveling Salesman Problem

    Directory of Open Access Journals (Sweden)

    H. Jafarzadeh

    2017-12-01

    Full Text Available The generalized traveling salesman problem (GTSP deals with finding the minimum-cost tour in a clustered set of cities. In this problem, the traveler is interested in finding the best path that goes through all clusters. As this problem is NP-hard, implementing a metaheuristic algorithm to solve the large scale problems is inevitable. The performance of these algorithms can be intensively promoted by other heuristic algorithms. In this study, a search method is developed that improves the quality of the solutions and competition time considerably in comparison with Genetic Algorithm. In the proposed algorithm, the genetic algorithms with the Nearest Neighbor Search (NNS are combined and a heuristic mutation operator is applied. According to the experimental results on a set of standard test problems with symmetric distances, the proposed algorithm finds the best solutions in most cases with the least computational time. The proposed algorithm is highly competitive with the published until now algorithms in both solution quality and running time.

  3. pulver: an R package for parallel ultra-rapid p-value computation for linear regression interaction terms.

    Science.gov (United States)

    Molnos, Sophie; Baumbach, Clemens; Wahl, Simone; Müller-Nurasyid, Martina; Strauch, Konstantin; Wang-Sattler, Rui; Waldenberger, Melanie; Meitinger, Thomas; Adamski, Jerzy; Kastenmüller, Gabi; Suhre, Karsten; Peters, Annette; Grallert, Harald; Theis, Fabian J; Gieger, Christian

    2017-09-29

    Genome-wide association studies allow us to understand the genetics of complex diseases. Human metabolism provides information about the disease-causing mechanisms, so it is usual to investigate the associations between genetic variants and metabolite levels. However, only considering genetic variants and their effects on one trait ignores the possible interplay between different "omics" layers. Existing tools only consider single-nucleotide polymorphism (SNP)-SNP interactions, and no practical tool is available for large-scale investigations of the interactions between pairs of arbitrary quantitative variables. We developed an R package called pulver to compute p-values for the interaction term in a very large number of linear regression models. Comparisons based on simulated data showed that pulver is much faster than the existing tools. This is achieved by using the correlation coefficient to test the null-hypothesis, which avoids the costly computation of inversions. Additional tricks are a rearrangement of the order, when iterating through the different "omics" layers, and implementing this algorithm in the fast programming language C++. Furthermore, we applied our algorithm to data from the German KORA study to investigate a real-world problem involving the interplay among DNA methylation, genetic variants, and metabolite levels. The pulver package is a convenient and rapid tool for screening huge numbers of linear regression models for significant interaction terms in arbitrary pairs of quantitative variables. pulver is written in R and C++, and can be downloaded freely from CRAN at https://cran.r-project.org/web/packages/pulver/ .

  4. Possible people, complaints, and the distinction between genetic planning and genetic engineering.

    Science.gov (United States)

    Delaney, James J

    2011-07-01

    Advances in the understanding of genetics have led to the belief that it may become possible to use genetic engineering to manipulate the DNA of humans at the embryonic stage to produce certain desirable traits. Although this currently cannot be done on a large scale, many people nevertheless object in principle to such practices. Most often, they argue that genetic enhancements would harm the children who were engineered, cause societal harms, or that the risks of perfecting the procedures are too high to proceed. However, many of these same people do not have serious objections to what is called 'genetic planning' procedures (such as the selection of sperm donors with desirable traits) that essentially have the same ends. The author calls the view that genetic engineering enhancements are impermissible while genetic planning enhancements are permissible the 'popular view', and argues that the typical reasons people give for the popular view fail to distinguish the two practices. This paper provides a principle that can salvage the popular view, which stresses that offspring from genetic engineering practices have grounds for complaint because they are identical to the pre-enhanced embryo, whereas offspring who are the result of genetic planning have no such grounds.

  5. Prospect of Induced Pluripotent Stem Cell Genetic Repair to Cure Genetic Diseases

    Directory of Open Access Journals (Sweden)

    Jeanne Adiwinata Pawitan

    2012-01-01

    Full Text Available In genetic diseases, where the cells are already damaged, the damaged cells can be replaced by new normal cells, which can be differentiated from iPSC. To avoid immune rejection, iPSC from the patient’s own cell can be developed. However, iPSC from the patients’s cell harbors the same genetic aberration. Therefore, before differentiating the iPSCs into required cells, genetic repair should be done. This review discusses the various technologies to repair the genetic aberration in patient-derived iPSC, or to prevent the genetic aberration to cause further damage in the iPSC-derived cells, such as Zn finger and TALE nuclease genetic editing, RNA interference technology, exon skipping, and gene transfer method. In addition, the challenges in using the iPSC and the strategies to manage the hurdles are addressed.

  6. Optimal groundwater remediation using artificial neural networks and the genetic algorithm

    Energy Technology Data Exchange (ETDEWEB)

    Rogers, Leah L. [Stanford Univ., CA (United States)

    1992-08-01

    An innovative computational approach for the optimization of groundwater remediation is presented which uses artificial neural networks (ANNs) and the genetic algorithm (GA). In this approach, the ANN is trained to predict an aspect of the outcome of a flow and transport simulation. Then the GA searches through realizations or patterns of pumping and uses the trained network to predict the outcome of the realizations. This approach has advantages of parallel processing of the groundwater simulations and the ability to ``recycle`` or reuse the base of knowledge formed by these simulations. These advantages offer reduction of computational burden of the groundwater simulations relative to a more conventional approach which uses nonlinear programming (NLP) with a quasi-newtonian search. Also the modular nature of this approach facilitates substitution of different groundwater simulation models.

  7. Optimal groundwater remediation using artificial neural networks and the genetic algorithm

    International Nuclear Information System (INIS)

    Rogers, L.L.

    1992-08-01

    An innovative computational approach for the optimization of groundwater remediation is presented which uses artificial neural networks (ANNs) and the genetic algorithm (GA). In this approach, the ANN is trained to predict an aspect of the outcome of a flow and transport simulation. Then the GA searches through realizations or patterns of pumping and uses the trained network to predict the outcome of the realizations. This approach has advantages of parallel processing of the groundwater simulations and the ability to ''recycle'' or reuse the base of knowledge formed by these simulations. These advantages offer reduction of computational burden of the groundwater simulations relative to a more conventional approach which uses nonlinear programming (NLP) with a quasi-newtonian search. Also the modular nature of this approach facilitates substitution of different groundwater simulation models

  8. Towards a genetic architecture of cryptic genetic variation and ...

    Indian Academy of Sciences (India)

    Unknown

    reprinted in this issue as a J. Genet. classic, pages 227–257). IAN DWORKIN* ... In this commentary, I will discuss the context of this work examining the genetic ... mental buffering where development was channelled in one of several ...

  9. Influence of genetic discrimination perceptions and knowledge on cancer genetics referral practice among clinicians.

    Science.gov (United States)

    Lowstuter, Katrina J; Sand, Sharon; Blazer, Kathleen R; MacDonald, Deborah J; Banks, Kimberly C; Lee, Carol A; Schwerin, Barbara U; Juarez, Margaret; Uman, Gwen C; Weitzel, Jeffrey N

    2008-09-01

    To describe nongenetics clinicians' perceptions and knowledge of cancer genetics and laws prohibiting genetic discrimination, attitudes toward the use of cancer genetic testing, and referral practices. Invitations to participate were sent to a random stratified sample of California Medical Association members and to all members of California Association of Nurse Practitioners and California Latino Medical Association. Responders in active practice were eligible and completed a 47-item survey. There were 1181 qualified participants (62% physicians). Although 96% viewed genetic testing as beneficial for their patients, 75% believed fear of genetic discrimination would cause patients to decline testing. More than 60% were not aware of federal or California laws prohibiting health insurance discrimination--concern about genetic discrimination was selected as a reason for nonreferral by 11%. A positive attitude toward genetic testing was the strongest predictor of referral (odds ratio: 3.55 [95% confidence interval: 2.24-5.63], P genetic discrimination, the less likely a participant was to refer (odds ratio: 0.72 [95% confidence interval: 0.518-0.991], P genetic discrimination law was associated with comfort recommending (odds ratio: 1.18 [95% confidence interval: 1.11-1.25], P genetic discrimination and knowledge deficits may be barriers to cancer genetics referrals. Clinician education may help promote access to cancer screening and prevention.

  10. Population Genetics of European Anchovy (Engraulis encrasicolus L. in the Seas of Turkey Based on Microsatellite DNA

    Directory of Open Access Journals (Sweden)

    Fevzi Bardakci

    2014-06-01

    Results: In this study, 13 microsatellite loci in 541 samples were analysed for determination of genetic structure of anchovy along Turkish coasts. The genetic variability was high among population, the average alleles numbers per locus per population ranged from 11.0 to 22.8. Observed heterozygosity per population was ranged from 0.612 (Mersin to 0.733 (İstanbul while expected heterozygosity was ranged from 0.774 (Mersin to 0.823 (Perşembe. The highest genetic distance was found between Antalya and Trabzon populations (FST=0.06949, the lowest between Antalya and İskenderun populations (0,00010. Analyses of 13 microsatellite loci were showed that there was low population structuring among all anchovy population (Fst: 0,024; SE 0,005. Although high genetic diversities was detected, for most loci with most populations were showed Hardy-Weinberg disequilibrium. Genetic distance analyses showed up Mediterranean specimens were highly distinct from Aegean and Black sea populations. Aegean populations were closer to Black sea populations because of higher gene flow between them rather than Mediterranean. A STRUCTURE computer program was indicated the presence of four possible genetic groups in Turkish territorial waters. Conclusions: Data to obtained from this study has found useful for the identification of genetic structuring of European anchovy distributed along the coasts of Turkish Seas. Results are also useful for planning of fishery management of anchovies in Turkey.

  11. Genetic Algorithms and Nucleation in VIH-AIDS transition.

    Science.gov (United States)

    Barranon, Armando

    2003-03-01

    VIH to AIDS transition has been modeled via a genetic algorithm that uses boom-boom principle and where population evolution is simulated with a cellular automaton based on SIR model. VIH to AIDS transition is signed by nucleation of infected cells and low probability of infection are obtained for different mutation rates in agreement with clinical results. A power law is obtained with a critical exponent close to the critical exponent of cubic, spherical percolation, colossal magnetic resonance, Ising Model and liquid-gas phase transition in heavy ion collisions. Computations were carried out at UAM-A Supercomputing Lab and author acknowledges financial support from Division of CBI at UAM-A.

  12. SBOL Visual: A Graphical Language for Genetic Designs

    Science.gov (United States)

    Adler, Aaron; Beal, Jacob; Bhatia, Swapnil; Cai, Yizhi; Chen, Joanna; Clancy, Kevin; Galdzicki, Michal; Hillson, Nathan J.; Le Novère, Nicolas; Maheshwari, Akshay J.; McLaughlin, James Alastair; Myers, Chris J.; P, Umesh; Pocock, Matthew; Rodriguez, Cesar; Soldatova, Larisa; Stan, Guy-Bart V.; Swainston, Neil; Wipat, Anil; Sauro, Herbert M.

    2015-01-01

    Synthetic Biology Open Language (SBOL) Visual is a graphical standard for genetic engineering. It consists of symbols representing DNA subsequences, including regulatory elements and DNA assembly features. These symbols can be used to draw illustrations for communication and instruction, and as image assets for computer-aided design. SBOL Visual is a community standard, freely available for personal, academic, and commercial use (Creative Commons CC0 license). We provide prototypical symbol images that have been used in scientific publications and software tools. We encourage users to use and modify them freely, and to join the SBOL Visual community: http://www.sbolstandard.org/visual. PMID:26633141

  13. Genetic Engineering

    Science.gov (United States)

    Phillips, John

    1973-01-01

    Presents a review of genetic engineering, in which the genotypes of plants and animals (including human genotypes) may be manipulated for the benefit of the human species. Discusses associated problems and solutions and provides an extensive bibliography of literature relating to genetic engineering. (JR)

  14. The genetical theory of social behaviour.

    Science.gov (United States)

    Lehmann, Laurent; Rousset, François

    2014-05-19

    We survey the population genetic basis of social evolution, using a logically consistent set of arguments to cover a wide range of biological scenarios. We start by reconsidering Hamilton's (Hamilton 1964 J. Theoret. Biol. 7, 1-16 (doi:10.1016/0022-5193(64)90038-4)) results for selection on a social trait under the assumptions of additive gene action, weak selection and constant environment and demography. This yields a prediction for the direction of allele frequency change in terms of phenotypic costs and benefits and genealogical concepts of relatedness, which holds for any frequency of the trait in the population, and provides the foundation for further developments and extensions. We then allow for any type of gene interaction within and between individuals, strong selection and fluctuating environments and demography, which may depend on the evolving trait itself. We reach three conclusions pertaining to selection on social behaviours under broad conditions. (i) Selection can be understood by focusing on a one-generation change in mean allele frequency, a computation which underpins the utility of reproductive value weights; (ii) in large populations under the assumptions of additive gene action and weak selection, this change is of constant sign for any allele frequency and is predicted by a phenotypic selection gradient; (iii) under the assumptions of trait substitution sequences, such phenotypic selection gradients suffice to characterize long-term multi-dimensional stochastic evolution, with almost no knowledge about the genetic details underlying the coevolving traits. Having such simple results about the effect of selection regardless of population structure and type of social interactions can help to delineate the common features of distinct biological processes. Finally, we clarify some persistent divergences within social evolution theory, with respect to exactness, synergies, maximization, dynamic sufficiency and the role of genetic arguments.

  15. The Algorithm for Algorithms: An Evolutionary Algorithm Based on Automatic Designing of Genetic Operators

    Directory of Open Access Journals (Sweden)

    Dazhi Jiang

    2015-01-01

    Full Text Available At present there is a wide range of evolutionary algorithms available to researchers and practitioners. Despite the great diversity of these algorithms, virtually all of the algorithms share one feature: they have been manually designed. A fundamental question is “are there any algorithms that can design evolutionary algorithms automatically?” A more complete definition of the question is “can computer construct an algorithm which will generate algorithms according to the requirement of a problem?” In this paper, a novel evolutionary algorithm based on automatic designing of genetic operators is presented to address these questions. The resulting algorithm not only explores solutions in the problem space like most traditional evolutionary algorithms do, but also automatically generates genetic operators in the operator space. In order to verify the performance of the proposed algorithm, comprehensive experiments on 23 well-known benchmark optimization problems are conducted. The results show that the proposed algorithm can outperform standard differential evolution algorithm in terms of convergence speed and solution accuracy which shows that the algorithm designed automatically by computers can compete with the algorithms designed by human beings.

  16. Application of genetic algorithm for the simultaneous identification of atmospheric pollution sources

    Science.gov (United States)

    Cantelli, A.; D'Orta, F.; Cattini, A.; Sebastianelli, F.; Cedola, L.

    2015-08-01

    A computational model is developed for retrieving the positions and the emission rates of unknown pollution sources, under steady state conditions, starting from the measurements of the concentration of the pollutants. The approach is based on the minimization of a fitness function employing a genetic algorithm paradigm. The model is tested considering both pollutant concentrations generated through a Gaussian model in 25 points in a 3-D test case domain (1000m × 1000m × 50 m) and experimental data such as the Prairie Grass field experiments data in which about 600 receptors were located along five concentric semicircle arcs and the Fusion Field Trials 2007. The results show that the computational model is capable to efficiently retrieve up to three different unknown sources.

  17. Inspirations in medical genetics.

    Science.gov (United States)

    Asadollahi, Reza

    2016-02-01

    There are abundant instances in the history of genetics and medical genetics to illustrate how curiosity, charisma of mentors, nature, art, the saving of lives and many other matters have inspired great discoveries. These achievements from deciphering genetic concepts to characterizing genetic disorders have been crucial for management of the patients. There remains, however, a long pathway ahead. © The Author(s) 2014.

  18. Genetic conservation and paddlefish propagation

    Science.gov (United States)

    Sloss, Brian L.; Klumb, Robert A.; Heist, Edward J.

    2009-01-01

    The conservation of genetic diversity of our natural resources is overwhelmingly one of the central foci of 21st century management practices. Three recommendations related to the conservation of paddlefish Polyodon spathula genetic diversity are to (1) identify genetic diversity at both nuclear and mitochondrial DNA loci using a suggested list of 20 sampling locations, (2) use genetic diversity estimates to develop genetic management units, and (3) identify broodstock sources to minimize effects of supplemental stocking on the genetic integrity of native paddlefish populations. We review previous genetic work on paddlefish and described key principles and concepts associated with maintaining genetic diversity within and among paddlefish populations and also present a genetic case study of current paddlefish propagation at the U.S. Fish and Wildlife Service Gavins Point National Fish Hatchery. This study confirmed that three potential sources of broodfish were genetically indistinguishable at the loci examined, allowing the management agencies cooperating on this program flexibility in sampling gametes. This study also showed significant bias in the hatchery occurred in terms of male reproductive contribution, which resulted in a shift in the genetic diversity of progeny compared to the broodfish. This shift was shown to result from differential male contributions, partially attributed to the mode of egg fertilization. Genetic insights enable implementation of a paddlefish propagation program within an adaptive management strategy that conserves inherent genetic diversity while achieving demographic goals.

  19. A High-Performance Genetic Algorithm: Using Traveling Salesman Problem as a Case

    Directory of Open Access Journals (Sweden)

    Chun-Wei Tsai

    2014-01-01

    Full Text Available This paper presents a simple but efficient algorithm for reducing the computation time of genetic algorithm (GA and its variants. The proposed algorithm is motivated by the observation that genes common to all the individuals of a GA have a high probability of surviving the evolution and ending up being part of the final solution; as such, they can be saved away to eliminate the redundant computations at the later generations of a GA. To evaluate the performance of the proposed algorithm, we use it not only to solve the traveling salesman problem but also to provide an extensive analysis on the impact it may have on the quality of the end result. Our experimental results indicate that the proposed algorithm can significantly reduce the computation time of GA and GA-based algorithms while limiting the degradation of the quality of the end result to a very small percentage compared to traditional GA.

  20. Multispecies genetic structure and hybridization in the Betula genus across Eurasia.

    Science.gov (United States)

    Tsuda, Yoshiaki; Semerikov, Vladimir; Sebastiani, Federico; Vendramin, Giovanni Giuseppe; Lascoux, Martin

    2017-01-01

    Boreal and cool temperate forests are the major land cover of northern Eurasia, and information about continental-scale genetic structure and past demographic history of forest species is important from an evolutionary perspective and has conservation implications. However, although many population genetic studies of forest tree species have been conducted in Europe or Eastern Asia, continental-scale genetic structure and past demographic history remain poorly known. Here, we focus on the birch genus Betula, which is commonly distributed in boreal and cool temperate forests, and examine 129 populations of two tetraploid and four diploid species collected from Iceland to Japan. All individuals were genotyped at seven to 18 nuclear simple sequence repeats (nSSRs). Pairwise FST' among the six species ranged from 0.285 to 0.903, and genetic differentiation among them was clear. structure analysis suggested that Betula pubescens is an allotetraploid and one of the parental species was Betula pendula. In both species pairs of B. pendula and B. plathyphylla, and B. pubescens and B. ermanii, genetic diversity was highest in central Siberia. A hybrid zone was detected around Lake Baikal for eastern and western species pairs regardless of ploidy level. Approximate Bayesian computation suggested that the divergence of B. pendula and B. platyphylla occurred around the beginning of the last ice age (36 300 years BP, 95% CI: 15 330-92 700) and hybridization between them was inferred to have occurred after the last glacial maximum (1614 years BP, 95% CI: 561-4710), with B. pendula providing a higher contribution to hybrids. © 2016 John Wiley & Sons Ltd.