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EBOOK
Title Probabilistic graphical models for genetics, genomics, and postgenomics / edited by Christine Sinoquet and Raphael Mourad.
Imprint Oxford : Oxford University Press, 2014.
2014

LOCATION CALL # STATUS MESSAGE
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LOCATION CALL # STATUS MESSAGE
 OHIOLINK OXFORD EBOOKS    ONLINE  
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Subject Genetics -- Mathematical models.
Genomics -- Statistical methods.
Genetics -- Statistical methods.
Graphical modeling (Statistics)
Computational Biology -- methods.
Models, Genetic.
Models, Statistical.
Genomics -- methods.
Bayes Theorem.
Computer Simulation.
Alt Name Sinoquet, Christine,
Mourad, Raphaël,
Description 1 online resource (XXVII, 449 pages) : illustrations
Bibliography Note Includes bibliographical references and index.
Summary At the crossroads between statistics and machine learning, probabilistic graphical models (PGMs) provide a powerful formal framework to model complex data. An expanding volume of biological data of various types, the so-called 'omics', is in need of accurate and efficient methods for modelling and PGMs are expected to have a prominent role to play. This book provides an overview of the applications of PGMs to genetics, genomics and postgenomics to meet this increased interest.
Contents Cover; A NOTE FROM THE EDITOR; PREFACE; CONTENTS; ABBREVIATIONS; LIST OF CONTRIBUTORS; Plates; Part I Introduction; 1 Probabilistic Graphical Models for Next-generation Genomics and Genetics; 1.1 Fine-grained Description of Living Systems; 1.1.1 DNA and the Genome; 1.1.2 Genes and Proteins; 1.1.3 Phenotype and Genotype; 1.1.4 Molecular Biology, Genetics, Genomics, and Postgenomics; 1.2 Higher Description Levels of Living Systems; 1.2.1 Complexity in Cells; 1.2.2 Genetics, Epigenetics, and Copy Number Polymorphism; 1.2.3 Epigenetics with Additional Prior Knowledge on the Genome.
1.2.4 Transcriptomics1.2.5 Transcriptomics with Prior Biological Knowledge; 1.2.6 Integrating Data from Several Levels; 1.2.7 Recapitulation; 1.3 An Era of High-throughput Genomic Technologies; 1.3.1 Genotyping; 1.3.2 Copy Number Polymorphism; 1.3.3 DNA Methylation Measurements; 1.3.4 Gene Expression Data; 1.3.5 Quantitative Trait Loci; 1.3.6 The Challenge of Handling Omics Data; 1.4 Probabilistic Graphical Models to Infer Novel Knowledge from Omics Data; 1.4.1 Gene Network Inference; 1.4.2 Causality Discovery; 1.4.3 Association Genetics; 1.4.4 Epigenetics.
1.4.5 Detection of Copy Number Variations1.4.6 Prediction of Outcomes from High-dimensional Genomic Data; 2 Essentials to Understand Probabilistic Graphical Models: A Tutorial about Inference and Learning; 2.1 Introduction; 2.2 Reminders; 2.3 Various Classes of Probabilistic Graphical Models; 2.3.1 Markov Chains and Hidden Markov Models; 2.3.2 Markov Random Fields; 2.3.3 Variants around the Concept of Markov random field; 2.3.4 Bayesian networks; 2.3.5 Unifying Model and Model Extension; 2.4 Probabilistic Inference; 2.4.1 Exact Inference; 2.4.2 Approximate Inference.
2.5 Learning Bayesian networks2.5.1 Parameter Learning; 2.5.2 Structure Learning; 2.6 Learning Markov random fields; 2.6.1 Parameter Learning; 2.6.2 Structure Learning; 2.7 Causal Networks; 2.8 List of General Monographs and Focused Chapter Books; Gene Expression; 3 Graphical Models and Multivariate Analysis of Microarray Data; 3.1 Introduction; 3.2 The Model; 3.3 Model Fitting; 3.3.1 Maximum Likelihood Estimation when the Zero Pattern is Known; 3.3.2 Determining the Pattern of Zeroes in the Inverse Covariance Matrix; 3.4 Hypothesis Testing; 3.4.1 Null Distributions by Permutation.
3.4.2 A Multivariate Test Statistic3.4.3 Partitioning of the Test Statistic; 3.4.4 Testing Strategies; 3.5 Example; 3.6 Discussion and Conclusions; 4 Comparison of Mixture Bayesian and Mixture Regression Approaches to Infer Gene Networks; 4.1 Introduction; 4.2 Methods; 4.2.1 Mixture Bayesian Network; 4.2.2 Mixture Regression Approach; 4.2.3 Data; 4.3 Results; 4.3.1 Comparison of Mixtures; 4.3.2 Mixture Modeling of Changes in Gene Relationships; 4.3.3 Interpretation of Mixtures; 4.3.4 Inference of Large Networks; 4.4 Conclusions.
ISBN 9780191779619 (electronic bk.)
019177961X (electronic bk.)
9780191019197 (electronic bk.)
0191019194 (electronic bk.)
9780198709022 (print)
0198709021 (hbk.)
OCLC # 898324747
Additional Format Print version 9780198709022



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