Bayesian integration of multi-way, multi-species, and time-series metabolomic datasets

Talk announcement:
HIIT Seminar Kumpula, Friday Nov 26, 10:15 a.m., Exactum C222

SPEAKER:
Ilkka Huopaniemi
Aalto University

TITLE:
Bayesian integration of multi-way, multi-species, and time-series
metabolomic datasets


ABSTRACT:
Multi-way analysis of variance (ANOVA) - type methods are the default
tool for modelling the effects of multiple covariates (disease,
treatments, gender, time-series) in populations of (biomedical)
continues-valued measurements. I present a multivariate Bayesian
modelling framework for multi-way modelling, that can deal with the main
restriction of modern biomedical data: small sample-size and high
dimensionality. I then describe how we've extended this framework to
analyze data from novel biomedical multi-way experiment types: (i)
integrating multiple data sources, (ii) integrating data from multiple
species, and (iii) time-series experiment with mixed aging- and disease
progression effects.


BIO:
Ilkka Huopaniemi is a PhD student at the Department of Information and
Computer Science at Aalto University, in the Statistical Machine
Learning and Bioinformatics Group lead by Samuel Kaski. He received his
M.Sc. degree in 2006 from the Department of Technical Physics and
Mathematics of TKK, and did his Master's thesis in the Statistical
Physics group. He's research interests are multi-way experimental
designs, Bayesian methods, metabolomics, data integration, translational
modelling.