Research Catalog
Probabilistic graphical models for genetics, genomics, and postgenomics
- Title
- Probabilistic graphical models for genetics, genomics, and postgenomics [electronic resource] / edited by Christine Sinoquet, editor-in-chief, and Raphaël Mourad, editor.
- Publication
- Oxford : Oxford University Press, 2014.
Details
- Additional Authors
- Description
- 1 online resource (xxvii, 449 pages, 4 unnumbered pages of plates) : illustrations (some color)
- Uniform Title
- Probabilistic graphical models for genetics, genomics, and postgenomics (Online)
- Subject
- Bibliography (note)
- Includes bibliographical references and index.
- Access (note)
- Access restricted to authorized users.
- Contents
- 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.
- LCCN
- 2013953773
- OCLC
- ssj0001514692
- Title
- Probabilistic graphical models for genetics, genomics, and postgenomics [electronic resource] / edited by Christine Sinoquet, editor-in-chief, and Raphaël Mourad, editor.
- Imprint
- Oxford : Oxford University Press, 2014.
- Edition
- First edition.
- Bibliography
- Includes bibliographical references and index.
- Access
- Access restricted to authorized users.
- Connect to:
- Added Author
- Sinoquet, Christine.Mourad, Raphaël.