Statistical Machine Learning for Genomics

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• Dr Christopher Yau (Centre for Computational Biology, University of Birmingham)
• Monday 09 April 2018, 11:00-12:00
• Computer Science, The Sloman Lounge (UG).

If you have a question about this talk, please contact Hector Basevi.

Host: Dr Iain Styles

Website: http://cwcyau.github.io/

Abstract: Genomic technologies have become an established and readily accessible tool for probing biological systems at the molecular level. Recent improvements in technology have lowered costs and increased throughput such that the classical problem of “large p, small n” is evolving to a “fixed p, increasing n” issue in a number of domains. In this talk I will describe some of the problems arising from genomics which are driving novel developments in statistical machine learning both in terms of novel models but also innovations in computation – particularly for Bayesian inference. I will outline some of my groups own contributions in modelling disease progression from high-dimensional sequencing data and structured dimensionality reduction techniques built for discrete genomic data.

This talk is part of the Artificial Intelligence and Natural Computation seminars series.

This talk is included in these lists:

• Artificial Intelligence and Natural Computation seminars
• Computer Science Departmental Series
• Computer Science Distinguished Seminars
• Computer Science, The Sloman Lounge (UG)

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