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.
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.
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. Raphaël 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.
Inhaltsangabe
* 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, Mickaël 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 Raphaël 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: Péter Antal, András Millinghoffer, Gábor Hullám, Gergely Hajós, Péter Sárközy, András Gézsi, Csaba Szalai and András Falus: Multilevel Analysis of Associations * V EPIGENETICS * 14: Meromit Singer and Lior Pachter: Bayesian Networks for DNA Methylation * 15: E. Andrés 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
* 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, Mickaël 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 Raphaël 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: Péter Antal, András Millinghoffer, Gábor Hullám, Gergely Hajós, Péter Sárközy, András Gézsi, Csaba Szalai and András Falus: Multilevel Analysis of Associations * V EPIGENETICS * 14: Meromit Singer and Lior Pachter: Bayesian Networks for DNA Methylation * 15: E. Andrés 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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