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Probabilistic Graphical Models for Genetics, Genomics, and Postgenomics$
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Christine Sinoquet and Raphaël Mourad

Print publication date: 2014

Print ISBN-13: 9780198709022

Published to Oxford Scholarship Online: December 2014

DOI: 10.1093/acprof:oso/9780198709022.001.0001

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Probabilistic Graphical Models for Next-generation Genomics and Genetics

Probabilistic Graphical Models for Next-generation Genomics and Genetics

Chapter:
(p.3) Chapter 1 Probabilistic Graphical Models for Next-generation Genomics and Genetics
Source:
Probabilistic Graphical Models for Genetics, Genomics, and Postgenomics
Author(s):

Christine Sinoquet

Publisher:
Oxford University Press
DOI:10.1093/acprof:oso/9780198709022.003.0001

The explosion in omics and other types of biological data has increased the demand for solid, large-scale statistical methods. These data can be discrete or continuous, dependent or independent, from many individuals or tissue types. There might be millions of correlated observations from a single individual, observations at different scales and levels, in addition to covariates. The study of living systems encompasses a wide range of concerns, from prospective to predictive and causal questions, reflecting the multiple interests in understanding biological mechanisms, disease etiology, predicting outcome, and deciphering causal relationships in data. Precisely, probabilistic graphical models provide a flexible statistical framework that is suitable to analyze such data. Notably, graphical models are able to handle dependences within data, which is an almost defining feature of cellular and other biological data.

Keywords:   living systems, biological complexity, high-throughput technologies, omics, probabilistic graphical models

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