Kernel Methods, Pattern Analysis, and Computational Metabolomics

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

Group web page at Aalto University 

KEPACO research group summer 2015

Where the find us

We are located at the Computer Science department of Aalto University


Last updated on 18 Dec 2015 by Juho Rousu - Page created on 15 May 2012 by Juho Rousu