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I INTRODUCTION; 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; V EPIGENETICS; 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

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