Probabilistic Graphical Models for Genetics, Genomics, and Postgenomics (Hardcover)
Language: English
Published by Oxford University Press, Oxford, 2014
- Hardcover
- New
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Hardcover. 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. 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. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…
Seller Inventory # 9780198709022
- Title
- Probabilistic Graphical Models for Genetics, Genomics, and Postgenomics (Hardcover)
- Author
- Christine Sinoquet
- Publisher
- Oxford University Press, Oxford
- Publication year
- 2014
- Condition
- new
- Binding
- Hardcover
- Language
- English
- ISBN 10
- 0198709021
- ISBN 13
- 9780198709022
"Synopsis" may belong to another edition of this title.
About the Author
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 for Computational 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) was at 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.
"About the title" may belong to another edition of this title.
Grand Eagle Retail
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AbeBooks seller since October 12, 2005
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