Probabilistic graphical models for genetics, genomics, and postgenomics /
| Other Authors: | , |
|---|---|
| Format: | Book |
| Language: | English |
| Published: |
Oxford ; New York :
Oxford University Press,
2014.
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| Edition: | First edition. |
| Subjects: |
Table of Contents:
- Pt. I. Introduction
- Probabilistic graphical models for next-generation genomics and genetics
- Essentials for 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.