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Probabilistic Graphical Models for Genetics, Genomics, and Postgenomics - Oxford Scholarship Online
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Probabilistic Graphical Models for Genetics, Genomics, and Postgenomics

Christine Sinoquet and Raphaël Mourad


At the crossroads between statistics and machine learning, probabilistic graphical models provide a powerful formal framework to model complex data. Probabilistic graphical models are probabilistic models whose graphical components denote conditional independence structures between random variables. The probabilistic framework makes it possible to deal with data uncertainty while the conditional independence assumption helps process high dimensional and complex data. Examples of probabilistic graphical models are Bayesian networks and Markov random fields, which represent two of the most popul ... More

Keywords: probabilistic graphical models, Bayesian networks, Markov random fields, complex data, high dimension, genetics, genomics, postgenomics

Bibliographic Information

Print publication date: 2014 Print ISBN-13: 9780198709022
Published to Oxford Scholarship Online: December 2014 DOI:10.1093/acprof:oso/9780198709022.001.0001


Affiliations are at time of print publication.

Christine Sinoquet, editor in chief
University of Nantes

Raphaël Mourad, editor
Department of Human Genetics, University of Chicago

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Part II Gene Expression

Chapter 5 Network Inference in Breast Cancer with Gaussian Graphical Models and Extensions

Marine Jeanmougin, Camille Charbonnier, Mickaël Guedj, and Julien Chiquet

Part III Causality Discovery

Chapter 7 Bayesian Causal Phenotype Network Incorporating Genetic Variation and Biological Knowledge

Jee Young Moon, Elias Chaibub Neto, Xinwei Deng, and Brian S. Yandellt

Part IV Genetic Association Studies

Chapter 13 Bayesian, Systems-based, Multilevel Analysis of Associations for Complex Phenotypes: from Interpretation to Decision

Péter Antal, András Millinghoffer, Gábor Hullám, Gergely Hajós, Péter Sárközy, András Gézsi, Csaba Szalai, and András Falus

Part V Epigenetics

Part VI Detection of Copy Number Variations

Part VII Prediction of Outcomes from High-dimensional Genomic Data

End Matter