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Machine learning methods for the analysis of heterogeneous, multi-source data

Machine learning methods for the analysis of heterogeneous, multi-source data. Ilkka Huopaniemi ilkka.huopaniemi@tkk.fi Statistical machine learning and bioinformatics group Prof. Samuel Kaski Department of information and computer science. Bioinformatics/Metabolomics.

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Machine learning methods for the analysis of heterogeneous, multi-source data

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

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

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

  4. 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)

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

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