Probabilistic graphical models for genetics, genomics, and postgenomics
Sinoquet
Mourad, RaphaëlContributor
Mourad, RaphaèelEditor
Sinoquet, ChristineContributor
Sinoquet, ChristineEditor
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.
Bibliographic Level Mode of Issuance: Monograph
Includes bibliographical references and index.
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.
Description based on online resource; title from home page (viewed on November 18, 2014).
pt. I. Introduction -- Probabilistic graphical models for next-generation genomics and genetics -- Essentials to understand probabilistic graphical models : a tutorial about inference and learning -- pt. II. Gene expression -- Graphical models and multivariate analysis of microarray data -- Comparison of mixture Bayesian and mixture regression approaches to infer gene networks -- Network inference in breast cancer with Gaussian graphical models and extensions -- pt. III. Causality discovery -- Utilizing genotypic information as a prior for learning gene networks -- Bayesian causal phenotype network incorporating genetic variation and biological knowledge -- Structural equation models for studying causal phenotype networks in quantitative genetics -- pt. IV. Genetic association studies -- Modeling linkage disequilibrium and performing association studies through probabilistic graphical models : a visiting tour of recent advances -- Modeling linkage disequilibrium with decomposable graphical models -- Scoring, searching and evaluating Bayesian network models of gene-phenotype association -- Graphical modeling of biological pathways in genome-wide association studies -- Bayesian systems-based, multilevel analysis of associations for complex phenotypes : from interpretation to decision -- pt. V. Epigenetics -- Bayesian networks in the study of genome-wide DNA methylation -- Latent variable models for analyzing DNA methylation -- pt. VI. Detection of copy number variations -- Detection of copy number variations from array comparative genomic hybridization data using linear-chain conditional random field models -- pt. VII. Prediction of outcomes from high-dimensional genomic data -- Prediction of clinical outcomes from genome-wide data.
Genomics
Genetics
Graphical modeling (Statistics)
Probability
Statistics as Topic
Biology
Models, Theoretical
Genetics
Models, Biological
Computing Methodologies
Investigative Techniques
Biological Science Disciplines
Epidemiologic Methods
Information Science
Health Care Evaluation Mechanisms
Analytical, Diagnostic and Therapeutic Techniques and Equipment
Natural Science Disciplines
Public Health
Quality of Health Care
Health Care Quality, Access, and Evaluation
Disciplines and Occupations
Environment and Public Health
Health Care
Models, Statistical
Computer Simulation
Bayes Theorem
Methods
Models, Genetic
Genomics
Computational Biology
Biology
Health & Biological Sciences
Genetics