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Título : Statistical Genetics of Quantitative Traits : Linkage, Maps, and QTL Tipo de documento: documento electrónico Autores: Wu, Rongling ; SpringerLink (Online service) ; Casella, George ; Ma, Chang-Xing 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] / Wu, Rongling ; SpringerLink (Online service) ; Casella, George ; Ma, Chang-Xing . - 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
Signatura Medio Ubicación Sub-localización Sección Estado ningún ejemplar For Better or For Worse? Collaborative Couples in the Sciences / SpringerLink (Online service) ; Lykknes, Annette ; Opitz, Donald L ; Van Tiggelen, Brigitte (2012)
Título : For Better or For Worse? Collaborative Couples in the Sciences Tipo de documento: documento electrónico Autores: SpringerLink (Online service) ; Lykknes, Annette ; Opitz, Donald L ; Van Tiggelen, Brigitte 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) ; Lykknes, Annette ; Opitz, Donald L ; Van Tiggelen, Brigitte . - 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 Design Tipo de documento: documento electrónico Autores: Casella, George ; SpringerLink (Online service) Editorial: New York, NY : Springer New York Fecha de publicación: 2008 Colección: Springer Texts in Statistics, ISSN 1431-875X Número de páginas: XXIII, 307 p Il.: online resource ISBN/ISSN/DL: 978-0-387-75965-4 Idioma : Inglés (eng) Palabras clave: Statistics Human genetics Plant Animal anatomy Statistical Theory and Methods Genetics & Genomics Anatomy / Morphology Histology Clasificación: 51 Matemáticas Resumen: Although statistical design is one of the oldest branches of statistics, its importance is ever increasing, especially in the face of the data flood that often faces statisticians. It is important to recognize the appropriate design, and to understand how to effectively implement it, being aware that the default settings from a computer package can easily provide an incorrect analysis. The goal of this book is to describe the principles that drive good design, paying attention to both the theoretical background and the problems arising from real experimental situations. Designs are motivated through actual experiments, ranging from the timeless agricultural randomized complete block, to microarray experiments, which naturally lead to split plot designs and balanced incomplete blocks. George Casella is Distinguished Professor in the Department of Statistics at the University of Florida. He is active in many aspects of statistics, having contributed to theoretical statistics in the areas of decision theory and statistical confidence, to environmental statistics, and has more recently concentrated efforts in statistical genomics. He also maintains active research interests in the theory and application of Monte Carlo and other computationally intensive methods. He is listed as an ISI "Highly Cited Researcher." In other capacities, Professor Casella has served as Theory and Methods Editor of the Journal of the American Statistical Association, 1996-1999, Executive Editor of Statistical Science, 2001-2004, and Co-Editor of the Journal of the Royal Statistical Society, Series B, 2009-2012. He has served on the Board of Mathematical Sciences of the National Research Council, 1999-2003, and many committees of both the American Statistical Association and the Institute of Mathematical Statistics. Professor Casella has co-authored five textbooks: Variance Components, 1992; Theory of Point Estimation, Second Edition, 1998; Monte Carlo Statistical Methods, Second Edition, 2004; Statistical Inference, Second Edition, 2001, and Statistical Genomics of Complex Traits, 2007 Nota de contenido: Basics -- Completely Randomized Designs -- Complete Block Designs -- Interlude: Assessing the Effects of Blocking -- Split Plot Designs -- Confounding in Blocks En línea: http://dx.doi.org/10.1007/978-0-387-75965-4 Link: https://biblioteca.cunef.edu/gestion/catalogo/index.php?lvl=notice_display&id=34216 Statistical Design [documento electrónico] / Casella, George ; SpringerLink (Online service) . - New York, NY : Springer New York, 2008 . - XXIII, 307 p : online resource. - (Springer Texts in Statistics, ISSN 1431-875X) .
ISBN : 978-0-387-75965-4
Idioma : Inglés (eng)
Palabras clave: Statistics Human genetics Plant Animal anatomy Statistical Theory and Methods Genetics & Genomics Anatomy / Morphology Histology Clasificación: 51 Matemáticas Resumen: Although statistical design is one of the oldest branches of statistics, its importance is ever increasing, especially in the face of the data flood that often faces statisticians. It is important to recognize the appropriate design, and to understand how to effectively implement it, being aware that the default settings from a computer package can easily provide an incorrect analysis. The goal of this book is to describe the principles that drive good design, paying attention to both the theoretical background and the problems arising from real experimental situations. Designs are motivated through actual experiments, ranging from the timeless agricultural randomized complete block, to microarray experiments, which naturally lead to split plot designs and balanced incomplete blocks. George Casella is Distinguished Professor in the Department of Statistics at the University of Florida. He is active in many aspects of statistics, having contributed to theoretical statistics in the areas of decision theory and statistical confidence, to environmental statistics, and has more recently concentrated efforts in statistical genomics. He also maintains active research interests in the theory and application of Monte Carlo and other computationally intensive methods. He is listed as an ISI "Highly Cited Researcher." In other capacities, Professor Casella has served as Theory and Methods Editor of the Journal of the American Statistical Association, 1996-1999, Executive Editor of Statistical Science, 2001-2004, and Co-Editor of the Journal of the Royal Statistical Society, Series B, 2009-2012. He has served on the Board of Mathematical Sciences of the National Research Council, 1999-2003, and many committees of both the American Statistical Association and the Institute of Mathematical Statistics. Professor Casella has co-authored five textbooks: Variance Components, 1992; Theory of Point Estimation, Second Edition, 1998; Monte Carlo Statistical Methods, Second Edition, 2004; Statistical Inference, Second Edition, 2001, and Statistical Genomics of Complex Traits, 2007 Nota de contenido: Basics -- Completely Randomized Designs -- Complete Block Designs -- Interlude: Assessing the Effects of Blocking -- Split Plot Designs -- Confounding in Blocks En línea: http://dx.doi.org/10.1007/978-0-387-75965-4 Link: https://biblioteca.cunef.edu/gestion/catalogo/index.php?lvl=notice_display&id=34216 Ejemplares
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Título : Statistical Methods in Molecular Evolution Tipo de documento: documento electrónico Autores: Nielsen, Rasmus ; 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] / Nielsen, Rasmus ; 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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