Probabilistic Graphical Models for Genetics, Genomics and Postgenomics

Hardcover | October 18, 2014

EditorChristine Sinoquet

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Nowadays bioinformaticians and geneticists are faced with myriad high-throughput data usually presenting the characteristics of uncertainty, high dimensionality and large complexity. These data will only allow insights into this wealth of so-called "omics" data if represented by flexible and scalable models, prior to any further analysis. At the interface between statistics and machine learning, probabilistic graphical models (PGMs) represent a powerful formalism to discovercomplex networks of relations. These models are also amenable to incorporating a priori biological information. Network reconstruction from gene expression data represents perhaps the most emblematic area of research where PGMs have been successfully applied. However these models have also created renewed interest in genetics inthe broad sense, in particular regarding association genetics, causality discovery, prediction of outcomes, detection of copy number variations, and epigenetics. This book provides an overview of the applications of PGMs to genetics, genomics and postgenomics to meet this increased interest. A salient feature of bioinformatics, interdisciplinarity, reaches its limit when an intricate cooperation between domain specialists is requested. Currently, few people are specialists in the design of advanced methods using probabilistic graphical models for postgenomics or genetics. This bookdeciphers such models so that their perceived difficulty no longer hinders their use and focuses on fifteen illustrations showing the mechanisms behind the models. Probabilistic Graphical Models for Genetics, Genomics and Postgenomics covers six main themes:(1) Gene network inference(2) Causality discovery(3) Association genetics(4) Epigenetics(5) Detection of copy number variations(6) Prediction of outcomes from high-dimensional genomic data.Written by leading international experts, this is a collection of the most advanced work at the crossroads of probabilistic graphical models and genetics, genomics, and postgenomics. The self-contained chapters provide an enlightened account of the pros and cons of applying these powerfultechniques.

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Nowadays bioinformaticians and geneticists are faced with myriad high-throughput data usually presenting the characteristics of uncertainty, high dimensionality and large complexity. These data will only allow insights into this wealth of so-called "omics" data if represented by flexible and scalable models, prior to any further analys...

Christine Sinoquet is an Associate Professor in Computer Science at the University of Nantes, France, where she works in the area of bioinformatics and computational biology at the Computer Science Institute of Nantes-Atlantic. She holds a M.Sc. in Computer Science from the University of Rennes 1 and received her Ph.D. in Computer Sci...

other books by Christine Sinoquet

Format:HardcoverDimensions:464 pages, 9.69 × 7.44 × 0.01 inPublished:October 18, 2014Publisher:Oxford University PressLanguage:English

The following ISBNs are associated with this title:

ISBN - 10:0198709021

ISBN - 13:9780198709022

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Table of Contents

I: INTRODUCTION1. Christine Sinoquet: Probabilistic Graphical Models for Next Generation Genomics and Genetics2. Christine Sinoquet: Essentials for Probabilistic Graphical ModelsII: GENE EXPRESSION3. Harri Kiiveri: Graphical Models and Multivariate Analysis of Microarray Data4. Sandra L. Rodriguez-Zas and Bruce R. Southey: Comparison of Mixture Bayesian and Mixture Regression Approaches to infer Gene Networks5. Marine Jeanmougin, Camille Charbonnier, Mickael Guedj and Julien Chiquet: Network Inference in Breast Cancer with Gaussian Graphical Models and ExtensionsIII: CAUSALITY DISCOVERY6. Kyle Chipman and Ambuj Singh: Enhanced Learning for Gene Networks7. Jee Young Moon, Elias Chaibub Neto, Xinwei Deng and Brian S. Yandell: Causal Phenotype Network Inference8. Guilherme J. M. Rosa and Bruno D. Valente: Structural Equation Models for Causal Phenotype NetworksIV: GENETIC ASSOCIATION STUDIES9. Christine Sinoquet and Raphael Mourad: Probabilistic Graphical Models for Association Genetics10. Haley J. Abel and Alun Thomas: Decomposable Graphical Models to Model Genetical Data11. Xia Jiang, Shyam Visweswaran and Richard E. Neapolitan: Bayesian Networks for Association Genetics12. Min Chen, Judy Cho and Hongyu Zhao: Graphical Modeling of Biological Pathways13. Peter Antal, Andras Millinghoffer, Gabor Hullam, Gergely Hajos, Peter Sarkozy, Andras Gezsi, Csaba Szalai and Andras Falus: Multilevel Analysis of AssociationsV: EPIGENETICS14. Meromit Singer and Lior Pachter: Bayesian Networks for DNA Methylation15. E. Andres Houseman: Latent Variable Models for DNA MethylationVI: DETECTION OF COPY NUMBER VARIATIONS16. Xiaolin Yin and Jing Li: Detection of Copy Number VariationsVII: PREDICTION OF OUTCOMES FROM HIGH-DIMENSIONAL GENOMIC DATA17. Shyam Visweswaran: Prediction of Clinical Outcomes from Genome-wide Data