Probabilistic Graphical Models for Genetics, Genomics and Postgenomics

by Mourad, Editor Raphael; Sinoquet, Editor in Chief Christine

• ISBN13: 9780198709022
• ISBN10: 0198709021
• Format: Hardcover
• Copyright: 2014-11-25
• Publisher: Oxford University Press

Author Biography

Raphael Mourad, Post-doctoral Research Fellow, Department of Human Genetics, University of Chicago

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 has initiated 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 research activities 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.

Raphael 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.

Table of Contents

I INTRODUCTION
1. Probabilistic Graphical Models for Next Generation Genomics and Genetics, Christine Sinoquet
2. Essentials for Probabilistic Graphical Models, Christine Sinoquet
II GENE EXPRESSION
3. Graphical Models and Multivariate Analysis of Microarray Data, Harri Kiiveri
4. Comparison of Mixture Bayesian and Mixture Regression Approaches to infer Gene Networks, Sandra L. Rodriguez-Zas and Bruce R. Southey
5. Network Inference in Breast Cancer with Gaussian Graphical Models and Extensions, Marine Jeanmougin, Camille Charbonnier, Mickael Guedj and Julien Chiquet
III CAUSALITY DISCOVERY
6. Enhanced Learning for Gene Networks, Kyle Chipman and Ambuj Singh
7. Causal Phenotype Network Inference, Jee Young Moon, Elias Chaibub Neto, Xinwei Deng and Brian S. Yandell
8. Structural Equation Models for Causal Phenotype Networks, Guilherme J. M. Rosa and Bruno D. Valente
IV GENETIC ASSOCIATION STUDIES
9. Probabilistic Graphical Models for Association Genetics, Christine Sinoquet and Raphael Mourad
10. Decomposable Graphical Models to Model Genetical Data, Haley J. Abel and Alun Thomas
11. Bayesian Networks for Association Genetics, Xia Jiang, Shyam Visweswaran and Richard E. Neapolitan
12. Graphical Modeling of Biological Pathways, Min Chen, Judy Cho and Hongyu Zhao
13. Multilevel Analysis of Associations, Peter Antal, Andras Millinghoffer, Gabor Hullam, Gergely Hajos, Peter Sarkozy, Andras Gezsi, Csaba Szalai and Andras Falus
V EPIGENETICS
14. Bayesian Networks for DNA Methylation, Meromit Singer and Lior Pachter
15. Latent Variable Models for DNA Methylation, E. Andres Houseman
VI DETECTION OF COPY NUMBER VARIATIONS
16. Detection of Copy Number Variations, Xiaolin Yin and Jing Li
VII PREDICTION OF OUTCOMES FROM HIGH-DIMENSIONAL GENOMIC DATA
17. Prediction of Clinical Outcomes from Genome-wide Data, Shyam Visweswaran