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 discover complex 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 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..
Probabilistic Graphical Models for Genetics, Genomics and Postgenomics read online
Editor in Chief Christine Sinoquet, Editor Raphael Mourad ebooks downloads
Probabilistic Graphical Models for Genetics, Genomics and Postgenomics download
Editor in Chief Christine Sinoquet, Editor Raphael Mourad Probabilistic Graphical Models for Genetics, Genomics and Postgenomics free
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