CS Forum: Thomas Brouwer

Speaker: Thomas Brouwer
Speaker affiliation: University of Cambridge, UK
Host: Prof Samuel Kaski
Time: 12:15 (coffee at 12:00)
Venue: T6, CS building, Konemiehentie 2

Bayesian data integration by multiple matrix tri-factorisation

Abstract

The amount of biological -omics data has increased dramatically in recent years, allowing us to better understand biological processes. One of the main challenges now is to integrate these different datasets and draw meaningful conclusions. One way to do this is by using a family of algorithms called matrix factorisation. These methods aim to extract hidden patterns from a matrix by decomposing it into smaller matrices. By sharing these so-called latent factors, we can jointly study multiple datasets.

In this talk I will present our own model for data integration, based on Bayesian non-negative matrix tri-factorisation. This approach allows us to share more latent information than competing methods. We demonstrate our model on multiple drug sensitivity datasets, and showcase better predictive power in cross-validation. Furthermore we will consider the problem of integrating gene expression and methylation data.

Bio

Thomas Brouwer is a PhD student under Pietro Lio' in machine learning and bioinformatics at the Computer Laboratory, University of Cambridge, where he also obtained his BA in Computer Science in 2014. His research is focused on developing Bayesian probabilistic models for analysing and integrating biological datasets, mainly using matrix factorisation methods. He focuses on drug development datasets, in particular for drug combinations, repositioning, and sensitivity prediction.