A Graphical Modeling Framework to Study Complex Dependence Patterns in High-Dimensional Biological Data
[url=mailto:email@example.com]Dr. Laurent Briollais[/url] , University of Toronto
Graphical models have been one of the most efficient statistical tools used in the last twenty years for the analysis of complex structured high-dimensional data. Graphical models provide a probabilistic framework for making inference and representing the knowledge that we have about these complex structured data. In biological research and more particularly in the emerging -omics disciplines such as genomics, proteomics, metabolomics, transcriptomics, data are often generated from complex high throughput experiments and from complex experimental designs.
[url=http://www.genizon.com/]Genizon Biosciences Inc.[/url]
[url=http://www.tgen.org/]Translational Genomics Research Institute[/url]