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Link de la búsquedaPermutation Testing for Isotonic Inference on Association Studies in Genetics / Luigi Salmaso (2011)
Título : Permutation Testing for Isotonic Inference on Association Studies in Genetics Tipo de documento: documento electrónico Autores: Luigi Salmaso ; SpringerLink (Online service) ; Rosa Arboretti ; Livio Corain ; Dario Mazzaro Editorial: Berlin, Heidelberg : Springer Berlin Heidelberg Fecha de publicación: 2011 Colección: SpringerBriefs in Statistics, ISSN 2191-544X Número de páginas: VI, 72 p. 13 illus Il.: online resource ISBN/ISSN/DL: 978-3-642-20584-2 Idioma : Inglés (eng) Palabras clave: Pharmacy Statistics Human genetics Biotechnology Biology Technique Psychometrics for Life Sciences, Medicine, Health Sciences Genetics Biological Techniques Clasificación: 51 Matemáticas Resumen: The purpose of this book is to illustrate a new statistical approach to test allelic association and genotype-specific effects in the genetic study of diseases. There are some parametric and non-parametric methods available for this purpose. We deal with population-based association studies, but comparisons with other methods will also be drawn, analysing the advantages and disadvantages of each one, particularly with regard to power properties with small sample sizes. In this framework we will work out some nonparametric statistical permutation tests and likelihood-based tests to perform case-control analyses to study allelic association between marker, disease-gene and environmental factors. Permutation tests, in particular, will be extended to multivariate and more complex studies, where we deal with several genes and several alleles together. Furthermore, we show simulations under different assumptions on the genetic model and analyse real data sets by simply studying one locus with the permutation test Nota de contenido: Introduction -- Association Studies in Genetics -- The Nonparametric Permutation Methodology -- Statistical Problems of Allelic Association -- Power and Sample Size Simulations -- Case Study -- Conclusions -- References En línea: http://dx.doi.org/10.1007/978-3-642-20584-2 Link: https://biblioteca.cunef.edu/gestion/catalogo/index.php?lvl=notice_display&id=33425 Permutation Testing for Isotonic Inference on Association Studies in Genetics [documento electrónico] / Luigi Salmaso ; SpringerLink (Online service) ; Rosa Arboretti ; Livio Corain ; Dario Mazzaro . - Berlin, Heidelberg : Springer Berlin Heidelberg, 2011 . - VI, 72 p. 13 illus : online resource. - (SpringerBriefs in Statistics, ISSN 2191-544X) .
ISBN : 978-3-642-20584-2
Idioma : Inglés (eng)
Palabras clave: Pharmacy Statistics Human genetics Biotechnology Biology Technique Psychometrics for Life Sciences, Medicine, Health Sciences Genetics Biological Techniques Clasificación: 51 Matemáticas Resumen: The purpose of this book is to illustrate a new statistical approach to test allelic association and genotype-specific effects in the genetic study of diseases. There are some parametric and non-parametric methods available for this purpose. We deal with population-based association studies, but comparisons with other methods will also be drawn, analysing the advantages and disadvantages of each one, particularly with regard to power properties with small sample sizes. In this framework we will work out some nonparametric statistical permutation tests and likelihood-based tests to perform case-control analyses to study allelic association between marker, disease-gene and environmental factors. Permutation tests, in particular, will be extended to multivariate and more complex studies, where we deal with several genes and several alleles together. Furthermore, we show simulations under different assumptions on the genetic model and analyse real data sets by simply studying one locus with the permutation test Nota de contenido: Introduction -- Association Studies in Genetics -- The Nonparametric Permutation Methodology -- Statistical Problems of Allelic Association -- Power and Sample Size Simulations -- Case Study -- Conclusions -- References En línea: http://dx.doi.org/10.1007/978-3-642-20584-2 Link: https://biblioteca.cunef.edu/gestion/catalogo/index.php?lvl=notice_display&id=33425 Ejemplares
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Título : Statistical Genetics of Quantitative Traits : Linkage, Maps, and QTL Tipo de documento: documento electrónico Autores: Rongling Wu ; SpringerLink (Online service) ; George Casella ; Chang-Xing Ma Editorial: New York, NY : Springer New York Fecha de publicación: 2007 Colección: Statistics for Biology and Health, ISSN 1431-8776 Número de páginas: XVI, 368 p Il.: online resource ISBN/ISSN/DL: 978-0-387-68154-2 Idioma : Inglés (eng) Palabras clave: Life sciences Bioinformatics Biochemistry Plant genetics Animal Biomathematics Statistics Sciences Biochemistry, general Genetics and Genomics & Computational Biology/Bioinformatics for Sciences, Medicine, Health Population Dynamics Clasificación: 51 Matemáticas Resumen: The book introduces the basic concepts and methods that are useful in the statistical analysis and modeling of DNA-based marker and phenotypic data that arise in agriculture, forrestry, experimental biology, and other fields. It concentrates on the linkage analysis of markers, map construction and quantitative trait locus (QTL) mapping and assumes a background in regression analysis and maximum likelihood approaches. The strengths of this book lie in the construction of general models and algorithms for linkage analysis and QTL mapping in any kind of crossed pedigrees initiated with inbred lines of crops and plant and animal model systems or outbred lines in forest trees and wildlife species. The book includes a detailed description of each approach and the step-by-step demonstration of live-example analyses designed to explain the utilization and usefulness of statistical methods. The book also includes exercise sets and computer codes for all the analyses used. This book can serve as a textbook for graduates and senior undergraduates in genetics, agronomy, forest biology, plant breeding and animal sciences. It will also be useful to researchers and other professionals in the areas of statistics, biology and agriculture. Rongling Wu is Associate Professor of Statistics at the University of Florida, Gainesville. He currently serves as Associate Editor for six genetics and bioinformatics journals. Chang-Xing Ma is Assistant Professor of Biostatistics at the State University of New York at Buffalo. George Casella is Distinguished Professor of Statistics and Distinguished Member of the Genetics Institute at the Univesity of Florida, Gainesville. He is a fellow of the American Statistical Association and the Institute of Mathematical Sciences, and the author of four other statistics books Nota de contenido: Basic Genetics -- Basic Statistics -- Linkage Analysis and Map Construction -- A General Model for Linkage Analysis in Controlled Crosses -- Linkage Analysis with Recombinant Inbred Lines -- Linkage Analysis for Distorted and Misclassified Markers -- Special Considerations in Linkage Analysis -- Marker Analysis of Phenotypes -- The Structure of QTL Mapping -- Interval Mapping with Regression Analysis -- Interval Mapping by Maximum Likelihood Approach -- Threshold and Precision Analysis -- Composite QTL Mapping -- QTL Mapping in Outbred Pedigrees En línea: http://dx.doi.org/10.1007/978-0-387-68154-2 Link: https://biblioteca.cunef.edu/gestion/catalogo/index.php?lvl=notice_display&id=34506 Statistical Genetics of Quantitative Traits : Linkage, Maps, and QTL [documento electrónico] / Rongling Wu ; SpringerLink (Online service) ; George Casella ; Chang-Xing Ma . - New York, NY : Springer New York, 2007 . - XVI, 368 p : online resource. - (Statistics for Biology and Health, ISSN 1431-8776) .
ISBN : 978-0-387-68154-2
Idioma : Inglés (eng)
Palabras clave: Life sciences Bioinformatics Biochemistry Plant genetics Animal Biomathematics Statistics Sciences Biochemistry, general Genetics and Genomics & Computational Biology/Bioinformatics for Sciences, Medicine, Health Population Dynamics Clasificación: 51 Matemáticas Resumen: The book introduces the basic concepts and methods that are useful in the statistical analysis and modeling of DNA-based marker and phenotypic data that arise in agriculture, forrestry, experimental biology, and other fields. It concentrates on the linkage analysis of markers, map construction and quantitative trait locus (QTL) mapping and assumes a background in regression analysis and maximum likelihood approaches. The strengths of this book lie in the construction of general models and algorithms for linkage analysis and QTL mapping in any kind of crossed pedigrees initiated with inbred lines of crops and plant and animal model systems or outbred lines in forest trees and wildlife species. The book includes a detailed description of each approach and the step-by-step demonstration of live-example analyses designed to explain the utilization and usefulness of statistical methods. The book also includes exercise sets and computer codes for all the analyses used. This book can serve as a textbook for graduates and senior undergraduates in genetics, agronomy, forest biology, plant breeding and animal sciences. It will also be useful to researchers and other professionals in the areas of statistics, biology and agriculture. Rongling Wu is Associate Professor of Statistics at the University of Florida, Gainesville. He currently serves as Associate Editor for six genetics and bioinformatics journals. Chang-Xing Ma is Assistant Professor of Biostatistics at the State University of New York at Buffalo. George Casella is Distinguished Professor of Statistics and Distinguished Member of the Genetics Institute at the Univesity of Florida, Gainesville. He is a fellow of the American Statistical Association and the Institute of Mathematical Sciences, and the author of four other statistics books Nota de contenido: Basic Genetics -- Basic Statistics -- Linkage Analysis and Map Construction -- A General Model for Linkage Analysis in Controlled Crosses -- Linkage Analysis with Recombinant Inbred Lines -- Linkage Analysis for Distorted and Misclassified Markers -- Special Considerations in Linkage Analysis -- Marker Analysis of Phenotypes -- The Structure of QTL Mapping -- Interval Mapping with Regression Analysis -- Interval Mapping by Maximum Likelihood Approach -- Threshold and Precision Analysis -- Composite QTL Mapping -- QTL Mapping in Outbred Pedigrees En línea: http://dx.doi.org/10.1007/978-0-387-68154-2 Link: https://biblioteca.cunef.edu/gestion/catalogo/index.php?lvl=notice_display&id=34506 Ejemplares
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Título : The Fundamentals of Modern Statistical Genetics Tipo de documento: documento electrónico Autores: Nan M. Laird ; SpringerLink (Online service) Editorial: New York, NY : Springer New York Fecha de publicación: 2011 Colección: Statistics for Biology and Health, ISSN 1431-8776 Número de páginas: XIV, 226 p Il.: online resource ISBN/ISSN/DL: 978-1-4419-7338-2 Idioma : Inglés (eng) Palabras clave: Statistics Human genetics Epidemiology Biometrics (Biology) Biostatistics for Life Sciences, Medicine, Health Sciences Genetics Clasificación: 51 Matemáticas Resumen: This book covers the statistical models and methods that are used to understand human genetics, following the historical and recent developments of human genetics. Starting with Mendel’s first experiments to genome-wide association studies, the book describes how genetic information can be incorporated into statistical models to discover disease genes. All commonly used approaches in statistical genetics (e.g. aggregation analysis, segregation, linkage analysis, etc), are used, but the focus of the book is modern approaches to association analysis. Numerous examples illustrate key points throughout the text, both of Mendelian and complex genetic disorders. The intended audience is statisticians, biostatisticians, epidemiologists and quantitatively- oriented geneticists and health scientists wanting to learn about statistical methods for genetic analysis, whether to better analyze genetic data, or to pursue research in methodology. A background in intermediate level statistical methods is required. The authors include few mathematical derivations, and the exercises provide problems for students with a broad range of skill levels. No background in genetics is assumed. Dr. Laird is a Professor of Biostatistics in the Biostatistics Department at the Harvard School of Public Health. Dr. Laird has contributed to methodology in many different fields, including missing data, EM-algorithm, meta-analysis, statistical genetics, and has coauthored a book with Garrett Fitzmaurice and James Ware on Applied Longitudinal Analysis. She is the recipient of many awards and prizes, including Fellow of the American Statistical Association, the American Association for the Advancement of Science, the Florence Nightingale Award, and the Janet Norwood Award. Dr. Lange is an Associate Professor in the Biostatistics Department at the Harvard School of Public Health. After his PhD in Statistics at the University of Reading (UK), he has worked extensively in the field of statistical genetics. Dr. Lange has been the director of the Institute of Genome Mathematics at the University of Bonn and has received several awards in mathematics and genetics. Dr. Lange is the developer of the PBAT package Nota de contenido: Introduction to statistical genetics and background in molecular genetics -- Principles of inheritance: mendel's laws and genetic models -- Some basic concepts from population genetics -- Aggregation, heritability and segregation analysis: modeling genetic inheritance without genetic data -- The general concepts of gene mapping: Linkage, association, linkage disequilibrium and marker maps -- Basic concepts of linkage analysis -- The basics of genetic association analysis -- Population substructure in association studies -- Association analysis in family designs -- Advanced topics -- Genome wide assocation studies -- Looking toward the future En línea: http://dx.doi.org/10.1007/978-1-4419-7338-2 Link: https://biblioteca.cunef.edu/gestion/catalogo/index.php?lvl=notice_display&id=33160 The Fundamentals of Modern Statistical Genetics [documento electrónico] / Nan M. Laird ; SpringerLink (Online service) . - New York, NY : Springer New York, 2011 . - XIV, 226 p : online resource. - (Statistics for Biology and Health, ISSN 1431-8776) .
ISBN : 978-1-4419-7338-2
Idioma : Inglés (eng)
Palabras clave: Statistics Human genetics Epidemiology Biometrics (Biology) Biostatistics for Life Sciences, Medicine, Health Sciences Genetics Clasificación: 51 Matemáticas Resumen: This book covers the statistical models and methods that are used to understand human genetics, following the historical and recent developments of human genetics. Starting with Mendel’s first experiments to genome-wide association studies, the book describes how genetic information can be incorporated into statistical models to discover disease genes. All commonly used approaches in statistical genetics (e.g. aggregation analysis, segregation, linkage analysis, etc), are used, but the focus of the book is modern approaches to association analysis. Numerous examples illustrate key points throughout the text, both of Mendelian and complex genetic disorders. The intended audience is statisticians, biostatisticians, epidemiologists and quantitatively- oriented geneticists and health scientists wanting to learn about statistical methods for genetic analysis, whether to better analyze genetic data, or to pursue research in methodology. A background in intermediate level statistical methods is required. The authors include few mathematical derivations, and the exercises provide problems for students with a broad range of skill levels. No background in genetics is assumed. Dr. Laird is a Professor of Biostatistics in the Biostatistics Department at the Harvard School of Public Health. Dr. Laird has contributed to methodology in many different fields, including missing data, EM-algorithm, meta-analysis, statistical genetics, and has coauthored a book with Garrett Fitzmaurice and James Ware on Applied Longitudinal Analysis. She is the recipient of many awards and prizes, including Fellow of the American Statistical Association, the American Association for the Advancement of Science, the Florence Nightingale Award, and the Janet Norwood Award. Dr. Lange is an Associate Professor in the Biostatistics Department at the Harvard School of Public Health. After his PhD in Statistics at the University of Reading (UK), he has worked extensively in the field of statistical genetics. Dr. Lange has been the director of the Institute of Genome Mathematics at the University of Bonn and has received several awards in mathematics and genetics. Dr. Lange is the developer of the PBAT package Nota de contenido: Introduction to statistical genetics and background in molecular genetics -- Principles of inheritance: mendel's laws and genetic models -- Some basic concepts from population genetics -- Aggregation, heritability and segregation analysis: modeling genetic inheritance without genetic data -- The general concepts of gene mapping: Linkage, association, linkage disequilibrium and marker maps -- Basic concepts of linkage analysis -- The basics of genetic association analysis -- Population substructure in association studies -- Association analysis in family designs -- Advanced topics -- Genome wide assocation studies -- Looking toward the future En línea: http://dx.doi.org/10.1007/978-1-4419-7338-2 Link: https://biblioteca.cunef.edu/gestion/catalogo/index.php?lvl=notice_display&id=33160 Ejemplares
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Título : For Better or For Worse? Collaborative Couples in the Sciences Tipo de documento: documento electrónico Autores: SpringerLink (Online service) ; Annette Lykknes ; Donald L. Opitz ; Brigitte van Tiggelen Editorial: Basel : Springer Basel Fecha de publicación: 2012 Otro editor: Imprint: Springer Colección: Science Networks. Historical Studies, ISSN 1421-6329 num. 44 Número de páginas: XIV, 322 p Il.: online resource ISBN/ISSN/DL: 978-3-0348-0286-4 Idioma : Inglés (eng) Palabras clave: Mathematics History Chemistry Plant genetics Animal Physics of Mathematical Sciences Science Chemistry/Food Science, general Physics, Genetics and Genomics & Clasificación: 51 Matemáticas Resumen: In this volume, a distinguished set of international scholars examine the nature of collaboration between life partners in the sciences, with particular attention to the ways in which personal and professional dynamics can foster or inhibit scientific practice. Breaking from traditional gender analyses which focus on divisions of labor and the assignment of credit, the studies scrutinize collaboration as a variable process between partners living in the nineteenth and twentieth centuries who were married and divorced, heterosexual and homosexual, aristocratic and working-class and politically right and left. The contributors analyze cases shaped by their particular geographical locations, ranging from retreat settings like the English countryside and Woods Hole, Massachusetts, to university laboratories and urban centers in Berlin, Stockholm, Geneva and London. The volume demonstrates how the terms and meanings of collaboration, variably shaped by disciplinary imperatives, cultural mores, and the agency of the collaborators themselves, illuminate critical intellectual and institutional developments in the modern sciences Nota de contenido: Foreword, by S.G. Kohlstedt - 1. Introduction -- 2. The Making of a Bestseller: Alexander and Jane Marcet’s Conversations on Chemistry, by J.-J. Dreifuss and N.T. Sigrist. - 3. ‘Not merely wifely devotion’: Collaborating in the Construction of Science at Terling Place, by D.L. Opitz. - 4. The Mystery of the Nobel Laureate and His Vanishing Wife, by J. Harvey. - 5. Married for Science, Divorced for Love: Success and Failure in the Collaboration between Astrid Cleve and Hans von Euler-Chelpin, by K. Espmark and C. Nordlund. - 6. Ida and Walter Noddack through Better and Worse: An Arbeitsgemeinschaft in Chemistry, by B. Van Tiggelen and A. Lykknes. - 7. A Model Collaborative Couple in Genetics: Anna Rachel Whiting and Phineas Westcott Whiting’s Study of Sex Determination in Habrobracon, by M.L. Richmond. - 8. Social Reform Collaboration and Gendered Academization: Three Swedish Social Science Couples at the Turn of the Twentieth Century, by P. Wisselgren. - 9. Social Science Couples in Britain at the Turn of the Twentieth Century: Gender Divisions in Work and Marriage, by E.J. Yeo. - 10. Co-operative Comradeships versus Same-Sex Partnerships: Historicizing Collaboration among Homosexual Couples in the Sciences, by D.L. Opitz - Epilogue: Collaborative Couples – Past, Present and Future, by N.G. Slack. - Select Bibliography -- Contributor Biographies -- Index En línea: http://dx.doi.org/10.1007/978-3-0348-0286-4 Link: https://biblioteca.cunef.edu/gestion/catalogo/index.php?lvl=notice_display&id=32874 For Better or For Worse? Collaborative Couples in the Sciences [documento electrónico] / SpringerLink (Online service) ; Annette Lykknes ; Donald L. Opitz ; Brigitte van Tiggelen . - Basel : Springer Basel : Imprint: Springer, 2012 . - XIV, 322 p : online resource. - (Science Networks. Historical Studies, ISSN 1421-6329; 44) .
ISBN : 978-3-0348-0286-4
Idioma : Inglés (eng)
Palabras clave: Mathematics History Chemistry Plant genetics Animal Physics of Mathematical Sciences Science Chemistry/Food Science, general Physics, Genetics and Genomics & Clasificación: 51 Matemáticas Resumen: In this volume, a distinguished set of international scholars examine the nature of collaboration between life partners in the sciences, with particular attention to the ways in which personal and professional dynamics can foster or inhibit scientific practice. Breaking from traditional gender analyses which focus on divisions of labor and the assignment of credit, the studies scrutinize collaboration as a variable process between partners living in the nineteenth and twentieth centuries who were married and divorced, heterosexual and homosexual, aristocratic and working-class and politically right and left. The contributors analyze cases shaped by their particular geographical locations, ranging from retreat settings like the English countryside and Woods Hole, Massachusetts, to university laboratories and urban centers in Berlin, Stockholm, Geneva and London. The volume demonstrates how the terms and meanings of collaboration, variably shaped by disciplinary imperatives, cultural mores, and the agency of the collaborators themselves, illuminate critical intellectual and institutional developments in the modern sciences Nota de contenido: Foreword, by S.G. Kohlstedt - 1. Introduction -- 2. The Making of a Bestseller: Alexander and Jane Marcet’s Conversations on Chemistry, by J.-J. Dreifuss and N.T. Sigrist. - 3. ‘Not merely wifely devotion’: Collaborating in the Construction of Science at Terling Place, by D.L. Opitz. - 4. The Mystery of the Nobel Laureate and His Vanishing Wife, by J. Harvey. - 5. Married for Science, Divorced for Love: Success and Failure in the Collaboration between Astrid Cleve and Hans von Euler-Chelpin, by K. Espmark and C. Nordlund. - 6. Ida and Walter Noddack through Better and Worse: An Arbeitsgemeinschaft in Chemistry, by B. Van Tiggelen and A. Lykknes. - 7. A Model Collaborative Couple in Genetics: Anna Rachel Whiting and Phineas Westcott Whiting’s Study of Sex Determination in Habrobracon, by M.L. Richmond. - 8. Social Reform Collaboration and Gendered Academization: Three Swedish Social Science Couples at the Turn of the Twentieth Century, by P. Wisselgren. - 9. Social Science Couples in Britain at the Turn of the Twentieth Century: Gender Divisions in Work and Marriage, by E.J. Yeo. - 10. Co-operative Comradeships versus Same-Sex Partnerships: Historicizing Collaboration among Homosexual Couples in the Sciences, by D.L. Opitz - Epilogue: Collaborative Couples – Past, Present and Future, by N.G. Slack. - Select Bibliography -- Contributor Biographies -- Index En línea: http://dx.doi.org/10.1007/978-3-0348-0286-4 Link: https://biblioteca.cunef.edu/gestion/catalogo/index.php?lvl=notice_display&id=32874 Ejemplares
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Título : Statistical Methods in Molecular Evolution Tipo de documento: documento electrónico Autores: Rasmus Nielsen ; SpringerLink (Online service) Editorial: New York, NY : Springer New York Fecha de publicación: 2005 Colección: Statistics for Biology and Health, ISSN 1431-8776 Número de páginas: XII, 505 p Il.: online resource ISBN/ISSN/DL: 978-0-387-27733-2 Idioma : Inglés (eng) Palabras clave: Life sciences Bioinformatics Evolutionary biology Plant genetics Biomathematics Statistics Sciences Biology for Sciences, Medicine, Health Genetics and Population Dynamics Mathematical Computational & Genomics Clasificación: 51 Matemáticas Resumen: In the field of molecular evolution, inferences about past evolutionary events are made using molecular data from currently living species. With the availability of genomic data from multiple related species, molecular evolution has become one of the most active and fastest growing fields of study in genomics and bioinformatics. Most studies in molecular evolution rely heavily on statistical procedures based on stochastic process modelling and advanced computational methods including high-dimensional numerical optimization and Markov Chain Monte Carlo. This book provides an overview of the statistical theory and methods used in studies of molecular evolution. It includes an introductory section suitable for readers that are new to the field, a section discussing practical methods for data analysis, and more specialized sections discussing specific models and addressing statistical issues relating to estimation and model choice. The chapters are written by the leaders in the field and they will take the reader from basic introductory material to the state-of the-art statistical methods. This book is suitable for statisticians seeking to learn more about applications in molecular evolution and molecular evolutionary biologists with an interest in learning more about the theory behind the statistical methods applied in the field. The chapters of the book assume no advanced mathematical skills beyond basic calculus, although familiarity with basic probability theory will help the reader. Most relevant statistical concepts are introduced in the book in the context of their application in molecular evolution, and the book should be accessible for most biology graduate students with an interest in quantitative methods and theory. Rasmus Nielsen received his Ph.D. form the University of California at Berkeley in 1998 and after a postdoc at Harvard University, he assumed a faculty position in Statistical Genomics at Cornell University. He is currently an Ole Rømer Fellow at the University of Copenhagen and holds a Sloan Research Fellowship. His is an associate editor of the Journal of Molecular Evolution and has published more than fifty original papers in peer-reviewed journals on the topic of this book Nota de contenido: Markov Models in Molecular Evolution -- to Applications of the Likelihood Function in Molecular Evolution -- to Markov Chain Monte Carlo Methods in Molecular Evolution -- Population Genetics of Molecular Evolution -- Practical Approaches for Data Analysis -- Maximum Likelihood Methods for Detecting Adaptive Protein Evolution -- HyPhy: Hypothesis Testing Using Phylogenies -- Bayesian Analysis of Molecular Evolution Using MrBayes -- Estimation of Divergence Times from Molecular Sequence Data -- Models of Molecular Evolution -- Markov Models of Protein Sequence Evolution -- Models of Microsatellite Evolution -- Genome Rearrangement -- Phylogenetic Hidden Markov Models -- Inferences on Molecular Evolution -- The Evolutionary Causes and Consequences of Base Composition Variation -- Statistical Alignment: Recent Progress, New Applications, and Challenges -- Estimating Substitution Matrices -- Posterior Mapping and Posterior Predictive Distributions -- Assessing the Uncertainty in Phylogenetic Inference En línea: http://dx.doi.org/10.1007/0-387-27733-1 Link: https://biblioteca.cunef.edu/gestion/catalogo/index.php?lvl=notice_display&id=35133 Statistical Methods in Molecular Evolution [documento electrónico] / Rasmus Nielsen ; SpringerLink (Online service) . - New York, NY : Springer New York, 2005 . - XII, 505 p : online resource. - (Statistics for Biology and Health, ISSN 1431-8776) .
ISBN : 978-0-387-27733-2
Idioma : Inglés (eng)
Palabras clave: Life sciences Bioinformatics Evolutionary biology Plant genetics Biomathematics Statistics Sciences Biology for Sciences, Medicine, Health Genetics and Population Dynamics Mathematical Computational & Genomics Clasificación: 51 Matemáticas Resumen: In the field of molecular evolution, inferences about past evolutionary events are made using molecular data from currently living species. With the availability of genomic data from multiple related species, molecular evolution has become one of the most active and fastest growing fields of study in genomics and bioinformatics. Most studies in molecular evolution rely heavily on statistical procedures based on stochastic process modelling and advanced computational methods including high-dimensional numerical optimization and Markov Chain Monte Carlo. This book provides an overview of the statistical theory and methods used in studies of molecular evolution. It includes an introductory section suitable for readers that are new to the field, a section discussing practical methods for data analysis, and more specialized sections discussing specific models and addressing statistical issues relating to estimation and model choice. The chapters are written by the leaders in the field and they will take the reader from basic introductory material to the state-of the-art statistical methods. This book is suitable for statisticians seeking to learn more about applications in molecular evolution and molecular evolutionary biologists with an interest in learning more about the theory behind the statistical methods applied in the field. The chapters of the book assume no advanced mathematical skills beyond basic calculus, although familiarity with basic probability theory will help the reader. Most relevant statistical concepts are introduced in the book in the context of their application in molecular evolution, and the book should be accessible for most biology graduate students with an interest in quantitative methods and theory. Rasmus Nielsen received his Ph.D. form the University of California at Berkeley in 1998 and after a postdoc at Harvard University, he assumed a faculty position in Statistical Genomics at Cornell University. He is currently an Ole Rømer Fellow at the University of Copenhagen and holds a Sloan Research Fellowship. His is an associate editor of the Journal of Molecular Evolution and has published more than fifty original papers in peer-reviewed journals on the topic of this book Nota de contenido: Markov Models in Molecular Evolution -- to Applications of the Likelihood Function in Molecular Evolution -- to Markov Chain Monte Carlo Methods in Molecular Evolution -- Population Genetics of Molecular Evolution -- Practical Approaches for Data Analysis -- Maximum Likelihood Methods for Detecting Adaptive Protein Evolution -- HyPhy: Hypothesis Testing Using Phylogenies -- Bayesian Analysis of Molecular Evolution Using MrBayes -- Estimation of Divergence Times from Molecular Sequence Data -- Models of Molecular Evolution -- Markov Models of Protein Sequence Evolution -- Models of Microsatellite Evolution -- Genome Rearrangement -- Phylogenetic Hidden Markov Models -- Inferences on Molecular Evolution -- The Evolutionary Causes and Consequences of Base Composition Variation -- Statistical Alignment: Recent Progress, New Applications, and Challenges -- Estimating Substitution Matrices -- Posterior Mapping and Posterior Predictive Distributions -- Assessing the Uncertainty in Phylogenetic Inference En línea: http://dx.doi.org/10.1007/0-387-27733-1 Link: https://biblioteca.cunef.edu/gestion/catalogo/index.php?lvl=notice_display&id=35133 Ejemplares
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