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Machine Learning Techniques to Identify Putative Genes Involved in Nitrogen Catabolite Repression in the Yeast Saccharomyces cerevisiae

Machine Learning Techniques to Identify Putative Genes Involved in Nitrogen Catabolite Repression in the Yeast Saccharomyces cerevisiae

This video was recorded at 1st International Workshop on Machine Learning in Systems Biology (MLSB), Evry 2007. Molecular biology and also all the biomedical sciences are undergoing a true revolution as a result of the emergence and growing impact of a series of new disciplines/tools sharing the "-omics" suffix in their name. These include in particular genomics, transcriptomics, proteomics and metabolomics devoted respectively to the examination of the entire systems of genes, transcripts, proteins and metabolites present in a given cell or tissue type. The availability of these new, highly effective tools for biological exploration is dramatically changing the way one performs research in at least two respects. First of all, the amount of available experimental data is not at all a limiting factor any more; on the contrary, there is a plethora of it. The challenge has shifted towards identifying the relevant pieces of information given the question, and how to make sense out of it (a "data mining" issue). Secondly, rather than to focus on components in isolation, we can now try to understand how biological systems behave as the result of the integration and interaction between the individual components that one can now monitor simultaneously (so called "systems biology"). Taking advantage of this wealth of "genomic" information has become a conditio sine qua non for whoever ambitions to remain competitive in molecular biology and more generally in biomedical sciences. Machine learning naturally appears as one of the main drivers of progress in this context, where most of the targets of interest deal with complex structured objects: sequences, 2D and 3D structures or interaction networks. At the same time bioinformatics and systems biology have already induced significant new developments of general interest in machine learning, for example in the context of learning with structured data, graph inference, semi-supervised learning, system identification, and novel combinations of optimization and learning algorithms. The aim of this workshop is to contribute to the cross-fertilization between the research in machine learning methods and their applications to complex biological and medical questions by bringing together method developers and experimentalists. We encourage submissions bringing forward methods for discovering complex structures (e.g. interaction networks, molecule structures) and methods supporting genome-wide data analysis.

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