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Link de la búsquedaBioinformatics and Computational Biology Solutions Using R and Bioconductor / SpringerLink (Online service) ; Robert Gentleman ; Vincent J. Carey ; Wolfgang Huber ; Rafael A. Irizarry ; Sandrine Dudoit (2005)
Título : Bioinformatics and Computational Biology Solutions Using R and Bioconductor Tipo de documento: documento electrónico Autores: SpringerLink (Online service) ; Robert Gentleman ; Vincent J. Carey ; Wolfgang Huber ; Rafael A. Irizarry ; Sandrine Dudoit Editorial: New York, NY : Springer New York Fecha de publicación: 2005 Colección: Statistics for Biology and Health, ISSN 1431-8776 Número de páginas: XX, 474 p. 128 illus. in color Il.: online resource ISBN/ISSN/DL: 978-0-387-29362-2 Idioma : Inglés (eng) Palabras clave: Computer science Bioinformatics Animal genetics Statistics Science Computational Biology/Bioinformatics for Life Sciences, Medicine, Health Sciences Genetics and Genomics Clasificación: 51 Matemáticas Resumen: Bioconductor is a widely used open source and open development software project for the analysis and comprehension of data arising from high-throughput experimentation in genomics and molecular biology. Bioconductor is rooted in the open source statistical computing environment R. This volume's coverage is broad and ranges across most of the key capabilities of the Bioconductor project, including importation and preprocessing of high-throughput data from microarray, proteomic, and flow cytometry platforms curation and delivery of biological metadata for use in statistical modeling and interpretation statistical analysis of high-throughput data, including machine learning and visualization, modeling and visualization of graphs and networks. The developers of the software, who are in many cases leading academic researchers, jointly authored chapters. All methods are illustrated with publicly available data, and a major section of the book is devoted to exposition of fully worked case studies. This book is more than a static collection of descriptive text, figures, and code examples that were run by the authors to produce the text; it is a dynamic document. Code underlying all of the computations that are shown is made available on a companion website, and readers can reproduce every number, figure, and table on their own computers. Robert Gentleman is Head of the Program in Computational Biology at the Fred Hutchinson Cancer Research Center in Seattle. He is one of the two authors of the original R system and a leading member of the R core team. Vincent Carey is Associate Professor of Medicine (Biostatistics), Channing Laboratory, Brigham and Women's Hospital, Harvard Medical School. Gentleman and Carey are co-founders of the Bioconductor project. Wolfgang Huber is Group Leader in the European Molecular Biology Laboratory at the European Bioinformatics Institute in Cambridge. He has made influential contributions to the error modeling of microarray data. Rafael Irizarry is Associate Professor of Biostatistics at the Johns Hopkins Bloomberg School of Public Health in Baltimore. He is co-developer of RMA and GCRMA, two of the most popular methodologies for preprocessing high-density oligonucleotide arrays. Sandrine Dudoit is Assistant Professor in the Department of Biostatistics at the University of California, Berkeley. She has made seminal discoveries in the fields of multiple testing and generalized cross-validation and spearheaded the deployment of these findings in applied genomic science Nota de contenido: Preprocessing data from genomic experiments -- Preprocessing Overview -- Preprocessing High-density Oligonucleotide Arrays -- Quality Assessment of Affymetrix GeneChip Data -- Preprocessing Two-Color Spotted Arrays -- Cell-Based Assays -- SELDI-TOF Mass Spectrometry Protein Data -- Meta-data: biological annotation and visualization -- Meta-data Resources and Tools in Bioconductor -- Querying On-line Resources -- Interactive Outputs -- Visualizing Data -- Statistical analysis for genomic experiments -- Analysis Overview -- Distance Measures in DNA Microarray Data Analysis -- Cluster Analysis of Genomic Data -- Analysis of Differential Gene Expression Studies -- Multiple Testing Procedures: the multtest Package and Applications to Genomics -- Machine Learning Concepts and Tools for Statistical Genomics -- Ensemble Methods of Computational Inference -- Browser-based Affymetrix Analysis and Annotation -- Graphs and networks -- and Motivating Examples -- Graphs -- Bioconductor Software for Graphs -- Case Studies Using Graphs on Biological Data -- Case studies -- limma: Linear Models for Microarray Data -- Classification with Gene Expression Data -- From CEL Files to Annotated Lists of Interesting Genes En línea: http://dx.doi.org/10.1007/0-387-29362-0 Link: https://biblioteca.cunef.edu/gestion/catalogo/index.php?lvl=notice_display&id=35165 Bioinformatics and Computational Biology Solutions Using R and Bioconductor [documento electrónico] / SpringerLink (Online service) ; Robert Gentleman ; Vincent J. Carey ; Wolfgang Huber ; Rafael A. Irizarry ; Sandrine Dudoit . - New York, NY : Springer New York, 2005 . - XX, 474 p. 128 illus. in color : online resource. - (Statistics for Biology and Health, ISSN 1431-8776) .
ISBN : 978-0-387-29362-2
Idioma : Inglés (eng)
Palabras clave: Computer science Bioinformatics Animal genetics Statistics Science Computational Biology/Bioinformatics for Life Sciences, Medicine, Health Sciences Genetics and Genomics Clasificación: 51 Matemáticas Resumen: Bioconductor is a widely used open source and open development software project for the analysis and comprehension of data arising from high-throughput experimentation in genomics and molecular biology. Bioconductor is rooted in the open source statistical computing environment R. This volume's coverage is broad and ranges across most of the key capabilities of the Bioconductor project, including importation and preprocessing of high-throughput data from microarray, proteomic, and flow cytometry platforms curation and delivery of biological metadata for use in statistical modeling and interpretation statistical analysis of high-throughput data, including machine learning and visualization, modeling and visualization of graphs and networks. The developers of the software, who are in many cases leading academic researchers, jointly authored chapters. All methods are illustrated with publicly available data, and a major section of the book is devoted to exposition of fully worked case studies. This book is more than a static collection of descriptive text, figures, and code examples that were run by the authors to produce the text; it is a dynamic document. Code underlying all of the computations that are shown is made available on a companion website, and readers can reproduce every number, figure, and table on their own computers. Robert Gentleman is Head of the Program in Computational Biology at the Fred Hutchinson Cancer Research Center in Seattle. He is one of the two authors of the original R system and a leading member of the R core team. Vincent Carey is Associate Professor of Medicine (Biostatistics), Channing Laboratory, Brigham and Women's Hospital, Harvard Medical School. Gentleman and Carey are co-founders of the Bioconductor project. Wolfgang Huber is Group Leader in the European Molecular Biology Laboratory at the European Bioinformatics Institute in Cambridge. He has made influential contributions to the error modeling of microarray data. Rafael Irizarry is Associate Professor of Biostatistics at the Johns Hopkins Bloomberg School of Public Health in Baltimore. He is co-developer of RMA and GCRMA, two of the most popular methodologies for preprocessing high-density oligonucleotide arrays. Sandrine Dudoit is Assistant Professor in the Department of Biostatistics at the University of California, Berkeley. She has made seminal discoveries in the fields of multiple testing and generalized cross-validation and spearheaded the deployment of these findings in applied genomic science Nota de contenido: Preprocessing data from genomic experiments -- Preprocessing Overview -- Preprocessing High-density Oligonucleotide Arrays -- Quality Assessment of Affymetrix GeneChip Data -- Preprocessing Two-Color Spotted Arrays -- Cell-Based Assays -- SELDI-TOF Mass Spectrometry Protein Data -- Meta-data: biological annotation and visualization -- Meta-data Resources and Tools in Bioconductor -- Querying On-line Resources -- Interactive Outputs -- Visualizing Data -- Statistical analysis for genomic experiments -- Analysis Overview -- Distance Measures in DNA Microarray Data Analysis -- Cluster Analysis of Genomic Data -- Analysis of Differential Gene Expression Studies -- Multiple Testing Procedures: the multtest Package and Applications to Genomics -- Machine Learning Concepts and Tools for Statistical Genomics -- Ensemble Methods of Computational Inference -- Browser-based Affymetrix Analysis and Annotation -- Graphs and networks -- and Motivating Examples -- Graphs -- Bioconductor Software for Graphs -- Case Studies Using Graphs on Biological Data -- Case studies -- limma: Linear Models for Microarray Data -- Classification with Gene Expression Data -- From CEL Files to Annotated Lists of Interesting Genes En línea: http://dx.doi.org/10.1007/0-387-29362-0 Link: https://biblioteca.cunef.edu/gestion/catalogo/index.php?lvl=notice_display&id=35165 Ejemplares
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Título : Bayesian Methods in Structural Bioinformatics Tipo de documento: documento electrónico Autores: SpringerLink (Online service) ; Thomas Hamelryck ; Kanti Mardia ; Jesper Ferkinghoff-Borg Editorial: Berlin, Heidelberg : Springer Berlin Heidelberg Fecha de publicación: 2012 Colección: Statistics for Biology and Health, ISSN 1431-8776 Número de páginas: XXII, 386 p Il.: online resource ISBN/ISSN/DL: 978-3-642-27225-7 Idioma : Inglés (eng) Palabras clave: Statistics Molecular biology Bioinformatics Biomathematics Biophysics Biological physics for Life Sciences, Medicine, Health Sciences Medicine and Physics Mathematical Computational Biology Biology/Bioinformatics Clasificación: 51 Matemáticas Resumen: This book is an edited volume, the goal of which is to provide an overview of the current state-of-the-art in statistical methods applied to problems in structural bioinformatics (and in particular protein structure prediction, simulation, experimental structure determination and analysis). It focuses on statistical methods that have a clear interpretation in the framework of statistical physics, rather than ad hoc, black box methods based on neural networks or support vector machines. In addition, the emphasis is on methods that deal with biomolecular structure in atomic detail. The book is highly accessible, and only assumes background knowledge on protein structure, with a minimum of mathematical knowledge. Therefore, the book includes introductory chapters that contain a solid introduction to key topics such as Bayesian statistics and concepts in machine learning and statistical physics Nota de contenido: Part I Foundations: An Overview of Bayesian Inference and Graphical Models -- Monte Carlo Methods for Inferences in High-dimensional Systems -- Part II Energy Functions for Protein Structure Prediction: On the Physical Relevance and Statistical Interpretation of Knowledge based Potentials -- Statistical Machine Learning of Protein Energetics from Experimentally Observed Structures -- A Statistical View on the Reference Ratio Method -- Part III Directional Statistics and Shape Theory: Statistical Modelling and Simulation Using the Fisher-Bingham Distribution -- Statistics of Bivariate von Mises Distributions -- Bayesian Hierarchical Alignment Methods -- Likelihood and Empirical Bayes Superpositions of Multiple Macromolecular Structures -- Part IV Graphical models for structure prediction: Probabilistic Models of Local Biomolecular Structure and their Application in Structural Simulation -- Prediction of Low Energy Protein Side Chain Configurations Using Markov Random Fields -- Part V Inferring Structure from Experimental Data -- Inferential Structure Determination from NMR Data -- Bayesian Methods in SAXS and SANS Structure Determination En línea: http://dx.doi.org/10.1007/978-3-642-27225-7 Link: https://biblioteca.cunef.edu/gestion/catalogo/index.php?lvl=notice_display&id=32965 Bayesian Methods in Structural Bioinformatics [documento electrónico] / SpringerLink (Online service) ; Thomas Hamelryck ; Kanti Mardia ; Jesper Ferkinghoff-Borg . - Berlin, Heidelberg : Springer Berlin Heidelberg, 2012 . - XXII, 386 p : online resource. - (Statistics for Biology and Health, ISSN 1431-8776) .
ISBN : 978-3-642-27225-7
Idioma : Inglés (eng)
Palabras clave: Statistics Molecular biology Bioinformatics Biomathematics Biophysics Biological physics for Life Sciences, Medicine, Health Sciences Medicine and Physics Mathematical Computational Biology Biology/Bioinformatics Clasificación: 51 Matemáticas Resumen: This book is an edited volume, the goal of which is to provide an overview of the current state-of-the-art in statistical methods applied to problems in structural bioinformatics (and in particular protein structure prediction, simulation, experimental structure determination and analysis). It focuses on statistical methods that have a clear interpretation in the framework of statistical physics, rather than ad hoc, black box methods based on neural networks or support vector machines. In addition, the emphasis is on methods that deal with biomolecular structure in atomic detail. The book is highly accessible, and only assumes background knowledge on protein structure, with a minimum of mathematical knowledge. Therefore, the book includes introductory chapters that contain a solid introduction to key topics such as Bayesian statistics and concepts in machine learning and statistical physics Nota de contenido: Part I Foundations: An Overview of Bayesian Inference and Graphical Models -- Monte Carlo Methods for Inferences in High-dimensional Systems -- Part II Energy Functions for Protein Structure Prediction: On the Physical Relevance and Statistical Interpretation of Knowledge based Potentials -- Statistical Machine Learning of Protein Energetics from Experimentally Observed Structures -- A Statistical View on the Reference Ratio Method -- Part III Directional Statistics and Shape Theory: Statistical Modelling and Simulation Using the Fisher-Bingham Distribution -- Statistics of Bivariate von Mises Distributions -- Bayesian Hierarchical Alignment Methods -- Likelihood and Empirical Bayes Superpositions of Multiple Macromolecular Structures -- Part IV Graphical models for structure prediction: Probabilistic Models of Local Biomolecular Structure and their Application in Structural Simulation -- Prediction of Low Energy Protein Side Chain Configurations Using Markov Random Fields -- Part V Inferring Structure from Experimental Data -- Inferential Structure Determination from NMR Data -- Bayesian Methods in SAXS and SANS Structure Determination En línea: http://dx.doi.org/10.1007/978-3-642-27225-7 Link: https://biblioteca.cunef.edu/gestion/catalogo/index.php?lvl=notice_display&id=32965 Ejemplares
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Título : Statistical Methods in Bioinformatics : An Introduction Tipo de documento: documento electrónico Autores: Warren J. Ewens ; SpringerLink (Online service) ; Gregory Grant Editorial: New York, NY : Springer New York Fecha de publicación: 2005 Colección: Statistics for Biology and Health, ISSN 1431-8776 Número de páginas: XX, 598 p Il.: online resource ISBN/ISSN/DL: 978-0-387-26648-0 Idioma : Inglés (eng) Palabras clave: Life sciences Epidemiology Bioinformatics Biochemistry Biomathematics Statistics Sciences Biochemistry, general Computational Biology/Bioinformatics Mathematical and Biology for Sciences, Medicine, Health Clasificación: 51 Matemáticas Resumen: Advances in computers and biotechnology have had a profound impact on biomedical research, and as a result complex data sets can now be generated to address extremely complex biological questions. Correspondingly, advances in the statistical methods necessary to analyze such data are following closely behind the advances in data generation methods. The statistical methods required by bioinformatics present many new and difficult problems for the research community. This book provides an introduction to some of these new methods. The main biological topics treated include sequence analysis, BLAST, microarray analysis, gene finding, and the analysis of evolutionary processes. The main statistical techniques covered include hypothesis testing and estimation, Poisson processes, Markov models and Hidden Markov models, and multiple testing methods. The second edition features new chapters on microarray analysis and on statistical inference, including a discussion of ANOVA, and discussions of the statistical theory of motifs and methods based on the hypergeometric distribution. Much material has been clarified and reorganized. The book is written so as to appeal to biologists and computer scientists who wish to know more about the statistical methods of the field, as well as to trained statisticians who wish to become involved with bioinformatics. The earlier chapters introduce the concepts of probability and statistics at an elementary level, but with an emphasis on material relevant to later chapters and often not covered in standard introductory texts. Later chapters should be immediately accessible to the trained statistician. Sufficient mathematical background consists of introductory courses in calculus and linear algebra. The basic biological concepts that are used are explained, or can be understood from the context, and standard mathematical concepts are summarized in an Appendix. Problems are provided at the end of each chapter allowing the reader to develop aspects of the theory outlined in the main text. Warren J. Ewens holds the Christopher H. Brown Distinguished Professorship at the University of Pennsylvania. He is the author of two books, Population Genetics and Mathematical Population Genetics. He is a senior editor of Annals of Human Genetics and has served on the editorial boards of Theoretical Population Biology, GENETICS, Proceedings of the Royal Society B and SIAM Journal in Mathematical Biology. He is a fellow of the Royal Society and the Australian Academy of Science. Gregory R. Grant is a senior bioinformatics researcher in the University of Pennsylvania Computational Biology and Informatics Laboratory. He obtained his Ph.D. in number theory from the University of Maryland in 1995 and his Masters in Computer Science from the University of Pennsylvania in 1999. Comments on the first edition: "This book would be an ideal text for a postgraduate course…[and] is equally well suited to individual study…. I would recommend the book highly." (Biometrics) "Ewens and Grant have given us a very welcome introduction to what is behind those pretty [graphical user] interfaces." (Naturwissenschaften) "The authors do an excellent job of presenting the essence of the material without getting bogged down in mathematical details." (Journal American Statistical Association) "The authors have restructured classical material to a great extent and the new organization of the different topics is one of the outstanding services of the book." (Metrika) Nota de contenido: Probability Theory (i): One Random Variable -- Probability Theory (ii): Many Random Variables -- Statistics (i): An Introduction to Statistical Inference -- Stochastic Processes (i): Poisson Processes and Markov Chains -- The Analysis of One DNA Sequence -- The Analysis of Multiple DNA or Protein Sequences -- Stochastic Processes (ii): Random Walks -- Statistics (ii): Classical Estimation Theory -- Statistics (iii): Classical Hypothesis Testing Theory -- BLAST -- Stochastic Processes (iii): Markov Chains -- Hidden Markov Models -- Gene Expression, Microarrays, and Multiple Testing -- Evolutionary Models -- Phylogenetic Tree Estimation En línea: http://dx.doi.org/10.1007/b137845 Link: https://biblioteca.cunef.edu/gestion/catalogo/index.php?lvl=notice_display&id=35101 Statistical Methods in Bioinformatics : An Introduction [documento electrónico] / Warren J. Ewens ; SpringerLink (Online service) ; Gregory Grant . - New York, NY : Springer New York, 2005 . - XX, 598 p : online resource. - (Statistics for Biology and Health, ISSN 1431-8776) .
ISBN : 978-0-387-26648-0
Idioma : Inglés (eng)
Palabras clave: Life sciences Epidemiology Bioinformatics Biochemistry Biomathematics Statistics Sciences Biochemistry, general Computational Biology/Bioinformatics Mathematical and Biology for Sciences, Medicine, Health Clasificación: 51 Matemáticas Resumen: Advances in computers and biotechnology have had a profound impact on biomedical research, and as a result complex data sets can now be generated to address extremely complex biological questions. Correspondingly, advances in the statistical methods necessary to analyze such data are following closely behind the advances in data generation methods. The statistical methods required by bioinformatics present many new and difficult problems for the research community. This book provides an introduction to some of these new methods. The main biological topics treated include sequence analysis, BLAST, microarray analysis, gene finding, and the analysis of evolutionary processes. The main statistical techniques covered include hypothesis testing and estimation, Poisson processes, Markov models and Hidden Markov models, and multiple testing methods. The second edition features new chapters on microarray analysis and on statistical inference, including a discussion of ANOVA, and discussions of the statistical theory of motifs and methods based on the hypergeometric distribution. Much material has been clarified and reorganized. The book is written so as to appeal to biologists and computer scientists who wish to know more about the statistical methods of the field, as well as to trained statisticians who wish to become involved with bioinformatics. The earlier chapters introduce the concepts of probability and statistics at an elementary level, but with an emphasis on material relevant to later chapters and often not covered in standard introductory texts. Later chapters should be immediately accessible to the trained statistician. Sufficient mathematical background consists of introductory courses in calculus and linear algebra. The basic biological concepts that are used are explained, or can be understood from the context, and standard mathematical concepts are summarized in an Appendix. Problems are provided at the end of each chapter allowing the reader to develop aspects of the theory outlined in the main text. Warren J. Ewens holds the Christopher H. Brown Distinguished Professorship at the University of Pennsylvania. He is the author of two books, Population Genetics and Mathematical Population Genetics. He is a senior editor of Annals of Human Genetics and has served on the editorial boards of Theoretical Population Biology, GENETICS, Proceedings of the Royal Society B and SIAM Journal in Mathematical Biology. He is a fellow of the Royal Society and the Australian Academy of Science. Gregory R. Grant is a senior bioinformatics researcher in the University of Pennsylvania Computational Biology and Informatics Laboratory. He obtained his Ph.D. in number theory from the University of Maryland in 1995 and his Masters in Computer Science from the University of Pennsylvania in 1999. Comments on the first edition: "This book would be an ideal text for a postgraduate course…[and] is equally well suited to individual study…. I would recommend the book highly." (Biometrics) "Ewens and Grant have given us a very welcome introduction to what is behind those pretty [graphical user] interfaces." (Naturwissenschaften) "The authors do an excellent job of presenting the essence of the material without getting bogged down in mathematical details." (Journal American Statistical Association) "The authors have restructured classical material to a great extent and the new organization of the different topics is one of the outstanding services of the book." (Metrika) Nota de contenido: Probability Theory (i): One Random Variable -- Probability Theory (ii): Many Random Variables -- Statistics (i): An Introduction to Statistical Inference -- Stochastic Processes (i): Poisson Processes and Markov Chains -- The Analysis of One DNA Sequence -- The Analysis of Multiple DNA or Protein Sequences -- Stochastic Processes (ii): Random Walks -- Statistics (ii): Classical Estimation Theory -- Statistics (iii): Classical Hypothesis Testing Theory -- BLAST -- Stochastic Processes (iii): Markov Chains -- Hidden Markov Models -- Gene Expression, Microarrays, and Multiple Testing -- Evolutionary Models -- Phylogenetic Tree Estimation En línea: http://dx.doi.org/10.1007/b137845 Link: https://biblioteca.cunef.edu/gestion/catalogo/index.php?lvl=notice_display&id=35101 Ejemplares
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Título : Evolutionary Computation for Modeling and Optimization Tipo de documento: documento electrónico Autores: Daniel Ashlock ; SpringerLink (Online service) Editorial: New York, NY : Springer New York Fecha de publicación: 2006 Número de páginas: XX, 572 p Il.: online resource ISBN/ISSN/DL: 978-0-387-31909-4 Idioma : Inglés (eng) Palabras clave: Mathematics Artificial intelligence Bioinformatics Applied mathematics Engineering Algorithms Applications of Intelligence (incl. Robotics) Clasificación: 51 Matemáticas Resumen: Evolutionary Computation for Optimization and Modeling is an introduction to evolutionary computation, a field which includes genetic algorithms, evolutionary programming, evolution strategies, and genetic programming. The text is a survey of some application of evolutionary algorithms. It introduces mutation, crossover, design issues of selection and replacement methods, the issue of populations size, and the question of design of the fitness function. It also includes a methodological material on efficient implementation. Some of the other topics in this book include the design of simple evolutionary algorithms, applications to several types of optimization, evolutionary robotics, simple evolutionary neural computation, and several types of automatic programming including genetic programming. The book gives applications to biology and bioinformatics and introduces a number of tools that can be used in biological modeling, including evolutionary game theory. Advanced techniques such as cellular encoding, grammar based encoding, and graph based evolutionary algorithms are also covered. This book presents a large number of homework problems, projects, and experiments, with a goal of illustrating single aspects of evolutionary computation and comparing different methods. Its readership is intended for an undergraduate or first-year graduate course in evolutionary computation for computer science, engineering, or other computational science students. Engineering, computer science, and applied math students will find this book a useful guide to using evolutionary algorithms as a problem solving tool Nota de contenido: An Overview of Evolutionary Computation -- Designing Simple Evolutionary Algorithms -- Optimizing Real-Valued Functions -- Sunburn: Coevolving Strings -- Small Neural Nets : Symbots -- Evolving Finite State Automata -- Ordered Structures -- Plus-One-Recall-Store -- Fitting to Data -- Tartarus: Discrete Robotics -- Evolving Logic Functions -- ISAc List: Alternative Genetic Programming -- Graph-Based Evolutionary Algorithms -- Cellular Encoding -- Application to Bioinformatics En línea: http://dx.doi.org/10.1007/0-387-31909-3 Link: https://biblioteca.cunef.edu/gestion/catalogo/index.php?lvl=notice_display&id=34792 Evolutionary Computation for Modeling and Optimization [documento electrónico] / Daniel Ashlock ; SpringerLink (Online service) . - New York, NY : Springer New York, 2006 . - XX, 572 p : online resource.
ISBN : 978-0-387-31909-4
Idioma : Inglés (eng)
Palabras clave: Mathematics Artificial intelligence Bioinformatics Applied mathematics Engineering Algorithms Applications of Intelligence (incl. Robotics) Clasificación: 51 Matemáticas Resumen: Evolutionary Computation for Optimization and Modeling is an introduction to evolutionary computation, a field which includes genetic algorithms, evolutionary programming, evolution strategies, and genetic programming. The text is a survey of some application of evolutionary algorithms. It introduces mutation, crossover, design issues of selection and replacement methods, the issue of populations size, and the question of design of the fitness function. It also includes a methodological material on efficient implementation. Some of the other topics in this book include the design of simple evolutionary algorithms, applications to several types of optimization, evolutionary robotics, simple evolutionary neural computation, and several types of automatic programming including genetic programming. The book gives applications to biology and bioinformatics and introduces a number of tools that can be used in biological modeling, including evolutionary game theory. Advanced techniques such as cellular encoding, grammar based encoding, and graph based evolutionary algorithms are also covered. This book presents a large number of homework problems, projects, and experiments, with a goal of illustrating single aspects of evolutionary computation and comparing different methods. Its readership is intended for an undergraduate or first-year graduate course in evolutionary computation for computer science, engineering, or other computational science students. Engineering, computer science, and applied math students will find this book a useful guide to using evolutionary algorithms as a problem solving tool Nota de contenido: An Overview of Evolutionary Computation -- Designing Simple Evolutionary Algorithms -- Optimizing Real-Valued Functions -- Sunburn: Coevolving Strings -- Small Neural Nets : Symbots -- Evolving Finite State Automata -- Ordered Structures -- Plus-One-Recall-Store -- Fitting to Data -- Tartarus: Discrete Robotics -- Evolving Logic Functions -- ISAc List: Alternative Genetic Programming -- Graph-Based Evolutionary Algorithms -- Cellular Encoding -- Application to Bioinformatics En línea: http://dx.doi.org/10.1007/0-387-31909-3 Link: https://biblioteca.cunef.edu/gestion/catalogo/index.php?lvl=notice_display&id=34792 Ejemplares
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Título : Modeling in Computational Biology and Biomedicine : A Multidisciplinary Endeavor Tipo de documento: documento electrónico Autores: SpringerLink (Online service) ; Frédéric Cazals ; Pierre Kornprobst Editorial: Berlin, Heidelberg : Springer Berlin Heidelberg Fecha de publicación: 2013 Otro editor: Imprint: Springer Número de páginas: XXVI, 318 p Il.: online resource ISBN/ISSN/DL: 978-3-642-31208-3 Idioma : Inglés (eng) Palabras clave: Mathematics Bioinformatics Applied mathematics Engineering Biomathematics Mathematical and Computational Biology Applications of Biology/Bioinformatics Clasificación: 51 Matemáticas Resumen: Computational biology, mathematical biology, biology and biomedicine are currently undergoing spectacular progresses due to a synergy between technological advances and inputs from physics, chemistry, mathematics, statistics and computer science. The goal of this book is to evidence this synergy by describing selected developments in the following fields: bioinformatics, biomedicine and neuroscience. This work is unique in two respects - first, by the variety and scales of systems studied and second, by its presentation: Each chapter provides the biological or medical context, follows up with mathematical or algorithmic developments triggered by a specific problem and concludes with one or two success stories, namely new insights gained thanks to these methodological developments. It also highlights some unsolved and outstanding theoretical questions, with a potentially high impact on these disciplines. Two communities will be particularly interested in this book. The first one is the vast community of applied mathematicians and computer scientists, whose interests should be captured by the added value generated by the application of advanced concepts and algorithms to challenging biological or medical problems. The second is the equally vast community of biologists. Whether scientists or engineers, they will find in this book a clear and self-contained account of concepts and techniques from mathematics and computer science, together with success stories on their favorite systems. The variety of systems described represents a panoply of complementary conceptual tools. On a practical level, the resources listed at the end of each chapter (databases, software) offer invaluable support for getting started on a specific topic in the fields of biomedicine, bioinformatics and neuroscience Nota de contenido: Foreword by Olivier Faugeras -- Foreword by Joël Janin -- Preface -- Part I Bioinformatics -- 1.Modeling Macro-molecular Complexes: a Journey Across Scales. F.Cazals, T.Dreyfus, and C.H. Robert -- 1.1.Introduction -- 1.2.Modeling Atomic Resolution -- 1.3.Modeling Large Assemblies -- 1.4.Outlook -- 1.5.Online Resources -- References -- 2.Modeling and Analysis of Gene Regulatory Networks. G.Bernot, J-P.Comet, A.Richard, M.Chaves, J-L.Gouzé, and F.Dayan -- 2.1.Introduction -- 2.2.Continuous and Hybrid Models of Genetic Regulatory Networks -- 2.3.Discrete Models of GRN -- 2.4.Outlook -- 2.5.Online Resources -- 2.6.Acknowledgments -- References -- Part II Biomedical Signal and Image Analysis -- 3.Noninvasive Cardiac Signal Analysis Using Data Decomposition Techniques. V.Zarzoso, O.Meste, P.Comon, D.G.Latcu, and N.Saoudi -- 3.1.Preliminaries and Motivation -- 3.2.T-Wave Alternans Detection via Principal Component Analysis -- 3.3.Atrial Activity Extraction via Independent Component Analysis -- 3.4.Conclusion and Outlook -- 3.5.Online Resources -- References -- 4.Deconvolution and Denoising for Confocal Microscopy. P.Pankajakshan, G.Engler, L.Blanc-Féraud, and J.Zerubia -- 4.1.Introduction -- 4.2.Development of the Auxiliary Computational Lens -- 4.3.Outlook -- 4.4.Selected Online Resources -- References -- 5.Statistical Shape Analysis of Surfaces in Medical Images Applied to the Tetralogy of Fallot Heart. K.McLeod, T.Mansi, M.Sermesant, G.Pongiglione, and X.Pennec -- 5.1.Introduction -- 5.2.Statistical Shape Analysis -- 5.3.Shape Analysis of ToF Data -- 5.4.Conclusion -- 5.5.Online Resources -- References -- 6.From Diffusion MRI to Brain Connectomics. A.Ghosh and R.Deriche -- 6.1.Introduction -- 6.2.A Brief History of NMR and MRI -- 6.3.Nuclear Magnetic Resonance and Diffusion -- 6.4.From Diffusion MRI to Tissue Microstructure -- 6.5.Computational Framework for Processing Diffusion MR Images -- 6.6.Tractography: Inferring the Connectivity -- 6.7.Clinical Applications 6.8.Conclusion -- 6.9.Online Resources -- References -- Part III Modeling in neuroscience -- 7.Single-Trial Analysis of Bioelectromagnetic Signals: The Quest for Hidden Information. M.Clerc, T.Papadopoulo, and C.Bénar -- 7.1.Introduction -- 7.2.Data-driven Approaches: Non-linear Dimensionality Reduction -- 7.3.Model-Driven Approaches: Matching Pursuit and its Extensions -- 7.4.Success Stories -- 7.5.Conclusion -- 7.6.Selected Online Resources -- References -- 8 Spike Train Statistics from Empirical Facts to Theory: The Case of the Retina. B.Cessac and A.Palacios -- 8.1.Introduction -- 8.2.Unraveling the Neural Code in the Retina via Spike Train Statistics Analysis -- 8.3.Spike Train Statistics from a Theoretical Perspective -- 8.4.Using Gibbs Distributions to Analysing Spike Trains Statistics -- 8.5.Conclusion -- 8.6.Outlook -- 8.7.Online Resources -- References -- Biology, Medicine and Biophysics -- Mathematics and Computer Science -- Index En línea: http://dx.doi.org/10.1007/978-3-642-31208-3 Link: https://biblioteca.cunef.edu/gestion/catalogo/index.php?lvl=notice_display&id=32513 Modeling in Computational Biology and Biomedicine : A Multidisciplinary Endeavor [documento electrónico] / SpringerLink (Online service) ; Frédéric Cazals ; Pierre Kornprobst . - Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 2013 . - XXVI, 318 p : online resource.
ISBN : 978-3-642-31208-3
Idioma : Inglés (eng)
Palabras clave: Mathematics Bioinformatics Applied mathematics Engineering Biomathematics Mathematical and Computational Biology Applications of Biology/Bioinformatics Clasificación: 51 Matemáticas Resumen: Computational biology, mathematical biology, biology and biomedicine are currently undergoing spectacular progresses due to a synergy between technological advances and inputs from physics, chemistry, mathematics, statistics and computer science. The goal of this book is to evidence this synergy by describing selected developments in the following fields: bioinformatics, biomedicine and neuroscience. This work is unique in two respects - first, by the variety and scales of systems studied and second, by its presentation: Each chapter provides the biological or medical context, follows up with mathematical or algorithmic developments triggered by a specific problem and concludes with one or two success stories, namely new insights gained thanks to these methodological developments. It also highlights some unsolved and outstanding theoretical questions, with a potentially high impact on these disciplines. Two communities will be particularly interested in this book. The first one is the vast community of applied mathematicians and computer scientists, whose interests should be captured by the added value generated by the application of advanced concepts and algorithms to challenging biological or medical problems. The second is the equally vast community of biologists. Whether scientists or engineers, they will find in this book a clear and self-contained account of concepts and techniques from mathematics and computer science, together with success stories on their favorite systems. The variety of systems described represents a panoply of complementary conceptual tools. On a practical level, the resources listed at the end of each chapter (databases, software) offer invaluable support for getting started on a specific topic in the fields of biomedicine, bioinformatics and neuroscience Nota de contenido: Foreword by Olivier Faugeras -- Foreword by Joël Janin -- Preface -- Part I Bioinformatics -- 1.Modeling Macro-molecular Complexes: a Journey Across Scales. F.Cazals, T.Dreyfus, and C.H. Robert -- 1.1.Introduction -- 1.2.Modeling Atomic Resolution -- 1.3.Modeling Large Assemblies -- 1.4.Outlook -- 1.5.Online Resources -- References -- 2.Modeling and Analysis of Gene Regulatory Networks. G.Bernot, J-P.Comet, A.Richard, M.Chaves, J-L.Gouzé, and F.Dayan -- 2.1.Introduction -- 2.2.Continuous and Hybrid Models of Genetic Regulatory Networks -- 2.3.Discrete Models of GRN -- 2.4.Outlook -- 2.5.Online Resources -- 2.6.Acknowledgments -- References -- Part II Biomedical Signal and Image Analysis -- 3.Noninvasive Cardiac Signal Analysis Using Data Decomposition Techniques. V.Zarzoso, O.Meste, P.Comon, D.G.Latcu, and N.Saoudi -- 3.1.Preliminaries and Motivation -- 3.2.T-Wave Alternans Detection via Principal Component Analysis -- 3.3.Atrial Activity Extraction via Independent Component Analysis -- 3.4.Conclusion and Outlook -- 3.5.Online Resources -- References -- 4.Deconvolution and Denoising for Confocal Microscopy. P.Pankajakshan, G.Engler, L.Blanc-Féraud, and J.Zerubia -- 4.1.Introduction -- 4.2.Development of the Auxiliary Computational Lens -- 4.3.Outlook -- 4.4.Selected Online Resources -- References -- 5.Statistical Shape Analysis of Surfaces in Medical Images Applied to the Tetralogy of Fallot Heart. K.McLeod, T.Mansi, M.Sermesant, G.Pongiglione, and X.Pennec -- 5.1.Introduction -- 5.2.Statistical Shape Analysis -- 5.3.Shape Analysis of ToF Data -- 5.4.Conclusion -- 5.5.Online Resources -- References -- 6.From Diffusion MRI to Brain Connectomics. A.Ghosh and R.Deriche -- 6.1.Introduction -- 6.2.A Brief History of NMR and MRI -- 6.3.Nuclear Magnetic Resonance and Diffusion -- 6.4.From Diffusion MRI to Tissue Microstructure -- 6.5.Computational Framework for Processing Diffusion MR Images -- 6.6.Tractography: Inferring the Connectivity -- 6.7.Clinical Applications 6.8.Conclusion -- 6.9.Online Resources -- References -- Part III Modeling in neuroscience -- 7.Single-Trial Analysis of Bioelectromagnetic Signals: The Quest for Hidden Information. M.Clerc, T.Papadopoulo, and C.Bénar -- 7.1.Introduction -- 7.2.Data-driven Approaches: Non-linear Dimensionality Reduction -- 7.3.Model-Driven Approaches: Matching Pursuit and its Extensions -- 7.4.Success Stories -- 7.5.Conclusion -- 7.6.Selected Online Resources -- References -- 8 Spike Train Statistics from Empirical Facts to Theory: The Case of the Retina. B.Cessac and A.Palacios -- 8.1.Introduction -- 8.2.Unraveling the Neural Code in the Retina via Spike Train Statistics Analysis -- 8.3.Spike Train Statistics from a Theoretical Perspective -- 8.4.Using Gibbs Distributions to Analysing Spike Trains Statistics -- 8.5.Conclusion -- 8.6.Outlook -- 8.7.Online Resources -- References -- Biology, Medicine and Biophysics -- Mathematics and Computer Science -- Index En línea: http://dx.doi.org/10.1007/978-3-642-31208-3 Link: https://biblioteca.cunef.edu/gestion/catalogo/index.php?lvl=notice_display&id=32513 Ejemplares
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