Probabilistic Graphical Models for Genetics, Genomics, and Postgenomics

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