Machine learning methods for the analysis of heterogeneous, multi- source data Ilkka Huopaniemi Statistical machine learning and bioinformatics group Prof. Samuel Kaski Department of information and computer science

Bioinformatics/Metabolomics Analysis of biological measurements (human patients, mice) Genomics, transcriptomics, proteomics, metabolomics, interactomics Metabolites are chemical compounds (lipids) Disease, treatments, time series

Bioinformatics data Noisy High dimensionality Often low number of samples Several data sources Advanced machine learning methods necessary Interpretation can be challenging

Dependency between data sets Commonalities in two different data sets with paired samples Bayesian models, machine learning Metabolomic measurement of blood and another tissue from each patient Integrating metabolomic and proteomic data Including prior knowledge (known pathway structures)

General things Tekes-project in collaboration with VTT (to get biological data) My second paper under way Planning a long research visit abroad