Computational Modelling Group

• Login/Register

You are here:

• CMG Home
• Events
• Discovering Transcriptional Modules by Bayesian Data Integration

Web page

http://www.isis.ecs.soton.ac.uk/

Submitter

Petrina Butler

Discovering Transcriptional Modules by Bayesian Data Integration

Speaker: Professor David Wild

Organiser: Dr. Sasan Mahmoodi

Time: 12/05/2010 14:00-16:00

Location: B6/1083

Abstract

We present a method for directly inferring transcriptional modules by integrating gene expression and transcription factor binding (ChIP-chip) data. Our model extends a hierarchical Dirichlet Process mixture model to allow data fusion on a gene-by-gene basis. This encodes the intuition that co-expression and co-regulation are not necessarily equivalent and hence we do not expect all genes to group similarly in both data sets. In particular, it allows us to identify the subset of genes that share the same structure of transcriptional modules in both data sets. We find that by working on a gene-by-gene basis, our model is able to extract clusters with greater functional coherence than existing methods. By combining gene expression and transcription factor binding (ChIP-chip) data in this way, we are better able to determine the groups of genes that are most likely to represent underlying transcriptional modules.

Speaker Biography

Professor David Wild

David Wild is a bioinformatician with extensive experience in structural and computational molecular biology. He has worked at the European Molecular Biology Laboratory, the Salk Institute, and in industry with Allelix Biopharmaceuticals, Oxford Molecular and GlaxoWellcome. He is currently Professor of Bioinformatics at the University of Warwick Systems Biology Centre and an adjunct Research Professor at the Keck Graduate Institute of Applied Life Sciences in Claremont, California. David holds a B.A. in Physics, an M.Sc. in Mathematics and a D.Phil. in Molecular Biophysics. His research interests encompass bioinformatics, systems and structural biology.