The KEPACO group develops machine learning methods, models and tools for data science, in particular computational metabolomics. The methodological backbone of the group is formed by kernel methods and regularized learning. The group focusses in learning with multiple and structured targets, multiple views and ensembles. Machine learning applications of interest include metabolite identification, metabolic network reconstruction and pathway analysis, chemogenomics as well as biomarker discovery.
Group leader: prof. Juho Rousu
Where the find us
Last updated on 18 Dec 2015 by Juho Rousu - Page created on 15 May 2012 by Juho Rousu