Description: Probabilistic Graphical Models for Genetics, Genomics, and Postgenomics by Christine Sinoquet, Raphaël Mourad 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 various types, the so-called omics, is in need of accurate and efficient methods for modelling and PGMs are expected to have a prominent role to play. FORMAT Hardcover LANGUAGE English CONDITION Brand New Publisher Description 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 apowerful formalism to discover complex networks of relations. These models are also amenable to incorporating a priori biological information. Network reconstruction from geneexpression 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 in the 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 designof advanced methods using probabilistic graphical models for postgenomics or genetics. This book deciphers 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, thisis 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 powerful techniques. Author Biography 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 Science from this same institution. During her Ph.D. position at the Inria Centre of Rennes, she specialized in bioinformatics. She hasinitiated two Master degree programs in bioinformatics (University of Clermont, France, and Nantes). She currently serves as the Head of this second Master degree program since 2005. Her researchactivities have been focused on various topics including data correction prior to molecular phylogeny inference, motif discovery in biological sequences, comparative genomics and imputation of missing genotypic data. Her current research interests are algorithmic and machine learning aspects of complex data analysis in the biomedical field. Raphaël Mourad received his PhD from the University of Nantes in september 2011. His first postdoc (2011-2012) was at the Lang Li lab, Center forComputational Biology and Bioinformatics, Indiana University Purdue University of Indianapolis (IUPUI). He notably worked on the genome-wide analysis of chromatin interactions. His second postdoc (2012-2013) wasat the Carole Ober Laboratory and Dan Nicolae Laboratory, Department of Human Genetics, University of Chicago. He worked on whole-genome sequencing data in asthma. As from november 2013, he started a third postdoc at the LIRMM, in Montpellier (France) which deals with the bioinformatics of HIV. 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, Mickaël 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 Raphaël 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: Péter Antal, András Millinghoffer, Gábor Hullám, Gergely Hajós, Péter Sárközy, András Gézsi, Csaba Szalai and András Falus: Multilevel Analysis of AssociationsV EPIGENETICS14: Meromit Singer and Lior Pachter: Bayesian Networks for DNA Methylation15: E. Andrés 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 Promotional A collection of the most advanced work at the crossroads of probabilistic graphical models and genetics, genomics, and postgenomics. Long Description 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 apowerful formalism to discover complex networks of relations. These models are also amenable to incorporating a priori biological information. Network reconstruction from geneexpression 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 in the 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 designof advanced methods using probabilistic graphical models for postgenomics or genetics. This book deciphers 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, thisis 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 powerful techniques. Feature This is the first book to provide an in-depth description of the mechanisms underlying cutting-edge methods using probabilistic graphical models for genetics, genomics and postgenomicsPromotes the use of powerful models through the provision of well documented examplesDemystifies probabilistic graphical models through a didactic expositionBridges the gap between different scientific worlds helping scientists to better communicate and contributes to the emergence of new transdisciplinary fields of researchProvides precise insights into applications in geneticsGives an idea of the huge potential of probabilistic graphical models in genetics, in the broad sense, but also in integrative biology and systems biology Details ISBN0198709021 Year 2014 ISBN-10 0198709021 ISBN-13 9780198709022 Format Hardcover Short Title PROBABILISTIC GRAPHICAL MODELS Language English Media Book Place of Publication Oxford Country of Publication United Kingdom DEWEY 576.5015192 UK Release Date 2014-09-18 NZ Release Date 2014-09-18 Author Raphaël Mourad Pages 478 Publisher Oxford University Press Publication Date 2014-09-18 Imprint Oxford University Press Edited by Raphaël Mourad Illustrations 99 b/w and 10 colour illustrations Audience Postgraduate, Research & Scholarly AU Release Date 2014-05-21 We've got this At The Nile, if you're looking for it, we've got it. 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ISBN-13: 9780198709022
Book Title: Probabilistic Graphical Models for Genetics, Genomics, and Postge
Number of Pages: 480 Pages
Language: English
Publication Name: Probabilistic Graphical Models for Genetics, Genomics, and Postgenomics
Publisher: Oxford University Press
Publication Year: 2014
Subject: Biology, Mathematics
Item Height: 249 mm
Item Weight: 1162 g
Type: Textbook
Author: Raphael Mourad
Item Width: 202 mm
Format: Hardcover
