PROBABILISTIC GRAPHICAL MODELS FOR GENETICS, GENOMICS, AND POSTGENOMICS 2014 (H)
|*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 postgenomics
*Promotes the use of powerful models through the provision of well documented examples
*Demystifies probabilistic graphical models through a didactic exposition
*Bridges the gap between different scientific worlds helping scientists to better communicate and contributes to the emergence of new transdisciplinary fields of research
*Provides precise insights into applications in genetics
*Gives an idea of the huge potential of probabilistic graphical models in genetics, in the broad sense, but also in integrative biology and systems biology
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
(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
1: Christine Sinoquet: Probabilistic Graphical Models for Next Generation Genomics and Genetics
2: Christine Sinoquet: Essentials for Probabilistic Graphical Models
II GENE EXPRESSION
3: Harri Kiiveri: Graphical Models and Multivariate Analysis of Microarray Data
4: Sandra L. Rodriguez-Zas and Bruce R. Southey: Comparison of Mixture Bayesian and Mixture Regression Approaches to infer Gene Networks
5: Marine Jeanmougin, Camille Charbonnier, Mickael Guedj and Julien Chiquet: Network Inference in Breast Cancer with Gaussian Graphical Models and Extensions
III CAUSALITY DISCOVERY
6: Kyle Chipman and Ambuj Singh: Enhanced Learning for Gene Networks
7: Jee Young Moon, Elias Chaibub Neto, Xinwei Deng and Brian S. Yandell: Causal Phenotype Network Inference
8: Guilherme J. M. Rosa and Bruno D. Valente: Structural Equation Models for Causal Phenotype Networks
IV GENETIC ASSOCIATION STUDIES
9: Christine Sinoquet and Raphael Mourad: Probabilistic Graphical Models for Association Genetics
10: Haley J. Abel and Alun Thomas: Decomposable Graphical Models to Model Genetical Data
11: Xia Jiang, Shyam Visweswaran and Richard E. Neapolitan: Bayesian Networks for Association Genetics
12: Min Chen, Judy Cho and Hongyu Zhao: Graphical Modeling of Biological Pathways
13: Peter Antal, Andras Millinghoffer, Gabor Hullam, Gergely Hajos, Peter Sarkozy, Andras Gezsi, Csaba Szalai and Andras Falus: Multilevel Analysis of Associations
14: Meromit Singer and Lior Pachter: Bayesian Networks for DNA Methylation
15: E. Andres Houseman: Latent Variable Models for DNA Methylation
VI DETECTION OF COPY NUMBER VARIATIONS
16: Xiaolin Yin and Jing Li: Detection of Copy Number Variations
VII PREDICTION OF OUTCOMES FROM HIGH-DIMENSIONAL GENOMIC DATA
17: Shyam Visweswaran: Prediction of Clinical Outcomes from Genome-wide Data