New Approaches to Biomedical Data Modelling: An Introductory Tutorial

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• Christopher M. Bishop (Microsoft Research, Cambridge)
• Monday 07 December 2009, 16:00-17:00
• Cancer Research UK Cambridge Research Institute, Lecture Theatre.

If you have a question about this talk, please contact Florian Markowetz.

The development of high-throughput genomic techniques, along with the emergence of integrated electronic health records, is creating new opportunities for biomedical data analysis and modelling. It also presents substantial challenges due to the large quantities of data, together with the need to incorporate complex biological prior knowledge.

Over the last five years a powerful new framework for machine learning has emerged, which exploits probabilistic graphical models to allow a deep integration of rich domain knowledge with statistical learning. Efficient new inference algorithms, based on local message-passing on the graph, allow this approach to be scaled to massive data sets. This framework has already achieved impressive technological successes, and is now being applied to biomedical problems, such as the integration of GWAS with detailed environmental and phenotype information, as well as rich prior knowledge, to investigate gene-environment interactions in childhood asthma.

This introductory tutorial will assume little or no previous knowledge of machine learning, and will be illustrated with both toy examples and real-world case studies.

This talk is part of the CRI Seminars on Quantitative Biology series.

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