University of Birmingham > Talks@bham > Artificial Intelligence and Natural Computation seminars > Statistical Machine Learning for Genomics

Statistical Machine Learning for Genomics

Add to your list(s) Download to your calendar using vCal

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

Host: Dr Iain Styles


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.

Tell a friend about this talk:

This talk is included in these lists:

Note that ex-directory lists are not shown.


Talks@bham, University of Birmingham. Contact Us | Help and Documentation | Privacy and Publicity.
talks@bham is based on from the University of Cambridge.