The Resource Probabilistic graphical models for genetics, genomics, and postgenomics, edited by Christine Sinoquet and Raphael Mourad

Probabilistic graphical models for genetics, genomics, and postgenomics, edited by Christine Sinoquet and Raphael Mourad

Label
Probabilistic graphical models for genetics, genomics, and postgenomics
Title
Probabilistic graphical models for genetics, genomics, and postgenomics
Statement of responsibility
edited by Christine Sinoquet and Raphael Mourad
Contributor
Editor
Subject
Language
eng
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
Cataloging source
NLGGC
Dewey number
576.501/5118
Illustrations
illustrations
Index
index present
LC call number
QH438.4.M3
LC item number
P76 2014
Literary form
non fiction
Nature of contents
  • dictionaries
  • bibliography
Label
Probabilistic graphical models for genetics, genomics, and postgenomics, edited by Christine Sinoquet and Raphael Mourad
Publication
Copyright
Bibliography note
Includes bibliographical references and index
http://library.link/vocab/branchCode
  • net
Carrier category
online resource
Carrier category code
cr
Carrier MARC source
rdacarrier
Content category
text
Content type code
txt
Content type MARC source
rdacontent
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
Control code
ocn898324747
Extent
1 online resource (XXVII, 449 pages)
Form of item
online
Isbn
9780191019197
Media category
computer
Media MARC source
rdamedia
Media type code
c
Other physical details
illustrations
http://library.link/vocab/recordID
.b33802087
Specific material designation
remote
System control number
  • (OCoLC)898324747
  • oso0191019194

Library Locations

    • Deakin University Library - Geelong Waurn Ponds CampusBorrow it
      75 Pigdons Road, Waurn Ponds, Victoria, 3216, AU
      -38.195656 144.304955
Processing Feedback ...