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Probabilistic Graphical Models for Genetics, Genomics and Postgenomics.

Author: Raphaël Mourad; Christine Sinoquet
Publisher: New York : Oxford University Press, Incorporated Nov. 2014.
Edition/Format:   eBook : Document : EnglishView all editions and formats
Summary:
At the crossroads between statistics and machine learning, probabilistic graphical models provide a powerful formal framework to model complex data. For instance, Bayesian networks and Markov random fields are two of the most popular probabilistic graphical models. With the rapid advance of high-throughput technologies and their ever decreasing costs, a fast-growing volume of biological data of various types - the  Read more...
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Material Type: Document, Internet resource
Document Type: Internet Resource, Computer File
All Authors / Contributors: Raphaël Mourad; Christine Sinoquet
ISBN: 9780198709022 0198709021
OCLC Number: 903243303
Target Audience: Scholarly & Professional
Description: 1 online resource
Contents: I INTRODUCTION; II GENE EXPRESSION; III CAUSALITY DISCOVERY; IV GENETIC ASSOCIATION STUDIES; V EPIGENETICS; VI DETECTION OF COPY NUMBER VARIATIONS; VII PREDICTION OF OUTCOMES FROM HIGH-DIMENSIONAL GENOMIC DATA

Abstract:

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  Read more...

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    schema:description "At the crossroads between statistics and machine learning, probabilistic graphical models provide a powerful formal framework to model complex data. For instance, Bayesian networks and Markov random fields are two of the most popular probabilistic graphical models. With the rapid advance of high-throughput technologies and their ever decreasing costs, a fast-growing volume of biological data of various types - the so-called ''omics'' - is in need of accurate andefficient methods for modeling, prior to further downstream analysis. As probabilistic graphical models are able to deal with high-dimensional data, it is foreseeable that such models will have a prominent role to play in advances in genome-wide data analyses. Currently, fewpeople are specialists in the design of cutting-edge methods using probabilistic graphical models for genetics, genomics and postgenomics. This seriously hinders the diffusion of such methods. The prime aim of the book is therefore to bring the concepts underlying these advanced models within reach of scientists who are not specialists of these models, but with no concession on the informativeness of the book. The target readers include researchers and engineers who have to design novelmethods for postgenomics data analysis, as well as graduate students starting a Masters or a PhD. In addition to an introductory chapter on probabilistic graphical models, a thorough review chapter focusing on selected domains in genetics and fourteen chapters illustrate the design of such advancedapproaches in various domains: gene network inference, inference of causal phenotype networks, association genetics, epigenetics, detection of copy number variations, and prediction of outcomes from high-dimensional genomic data. Notably, most examples also illustrate that probabilistic graphical models are well suited for integrative biology and systems biology, hot topics guaranteed to be of lasting interest."@en ;
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