Probabilistic Graphical Models for Genetics, Genomics, and Postgenomics
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Product details:
- Publisher OUP Oxford
- Date of Publication 18 September 2014
- ISBN 9780198709022
- Binding Hardback
- No. of pages478 pages
- Size 249x202x25 mm
- Weight 1162 g
- Language English
- Illustrations 99 b/w and 10 colour illustrations 0
Categories
Short description:
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.
MoreLong 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 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.
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 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, 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 powerful techniques.
Table of Contents:
I INTRODUCTION
Probabilistic Graphical Models for Next Generation Genomics and Genetics
Essentials for Probabilistic Graphical Models
II GENE EXPRESSION
Graphical Models and Multivariate Analysis of Microarray Data
Comparison of Mixture Bayesian and Mixture Regression Approaches to infer Gene Networks
Network Inference in Breast Cancer with Gaussian Graphical Models and Extensions
III CAUSALITY DISCOVERY
Enhanced Learning for Gene Networks
Causal Phenotype Network Inference
Structural Equation Models for Causal Phenotype Networks
IV GENETIC ASSOCIATION STUDIES
Probabilistic Graphical Models for Association Genetics
Decomposable Graphical Models to Model Genetical Data
Bayesian Networks for Association Genetics
Graphical Modeling of Biological Pathways
Multilevel Analysis of Associations
V EPIGENETICS
Bayesian Networks for DNA Methylation
Latent Variable Models for DNA Methylation
VI DETECTION OF COPY NUMBER VARIATIONS
Detection of Copy Number Variations
VII PREDICTION OF OUTCOMES FROM HIGH-DIMENSIONAL GENOMIC DATA
Prediction of Clinical Outcomes from Genome-wide Data
