Kernel Methods, Pattern Analysis and Computational Metabolomics (KEPACO)
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 particularly focusses in learning with multiple and structured targets, multiple views and ensembles. Applications of interest include metabolomics, biomedicine, pharmacology and synthetic biology.
See overview of KEPACO research (in PDF)
- KEPACoffee, regular group gathering
- June 25, 2020. KEPACO has its summer get-together on the beautiful Pentala island.
- June 15, 2020. Wen Xiang starts as a summer intern. Welcome Wen!
- May 29, 2020. Dr. Anna Cichonska receives the award for the best Bioinformatics thesisin Finland 2018-2019 for her thesis
Machine Learning for Systems Pharmacology. Congratulations, Anna!
- May 21-22, 2020. Tianduanyi Wang participates in the Bioinformatics Research and Education Workshop, BREW-2020
- March 9, 2020 Santeri Mentu starts his MSc thesis in KEPACO group. Welcome Santeri!
- March 5, 2020. Viivi Uurtio defends her PhD thesis Methods for Interpreting Kernel Canonical Correlation Measures
- January 2, 2020. Dr. Riikka Huusari starts as a post-doctoral researcher at KEPACO. Welcome Riikka!
- September 20, 2019. Juho co-chairs an ECML/PKDD workshop on Data and Machine Learning Advances with Multiple Views
- September 6, 2019. Heli Julkunen receives a MSc thesis award for her thesis "Predictive modeling of anticancer efficacy of drug combinations using factorization machines". Congrats!
- September 1, 2019. Dr. Kai Duehrkop joins the group as a post-doctoral researcher for Autumn 2019. Welcome Kai!
- August 30, 2019. Our summer interns Antoine, Jane and Luc told about their experience in Aalto university news article.
- August 19-September 6,2019. Eric visits the group of Dr. Simon Rogers at University of Glasgow.
- July 3, 2019. KEPACO group had their summer retreat at Hameenkyla Manor including some groupwork and Kin-ball.
- June 11, 2019. Viivi presents her paper "Large-Scale Sparse Kernel Canonical Correlation Analysis" at ICML'2019, Long Beach, California
- June 1, 2019. Jane Douat (Telecom Paristech, France) and Bugra Aker Yilmaz (METU, Turkey) start as summer interns at KEPACO. Welcome!
- May 6--May 31, 2019. Viivi visits University College London, UK
- April 8--June 20, 2019. Juho visits University of Glasgow, UK
- March 20, 2019. Anna received a Dissertation Award from Aalto School of Science. Congrats Anna!
- January 8--March 31, 2019. Juho visits Universite Laval, Quebec City, Canada
- Older news
- Juho Rousu, Professor, group leader
- Sandor Szedmak, PhD, senior research scientist
- Maryam Sabzevari, PhD, post-doctoral researcher
- Riikka Huusari, PhD, post-doctoral researcher
- Viivi Uurtio, PhD student
- Eric Bach, PhD student
- Tianduanyi Wang, PhD student (FIMM/HIIT)
- Luc Motte, Visiting PhD student (Telecom Paris)
- Santeri Mentu, MSc student
- Wen Xiang, MSc student
The KEPACO group is located at the Department of Computer
Science at the School of
Science of Aalto University. We
also belong to the Helsinki Institute
for Information Technology.
Contact information and how
to get to CS department in Aalto University Otaniemi Campus
- MAGITICS - MAchine learning for diGItal diagnosTICS of antimicrobial resistance, JPI/Academy of Finland grant, 2020-2022
- AI4Synbio - Artificial Intelligence for Synthetic Biology, SITRA grant, 2018-2019
- TensorBiomed - Tensor Learning for Biomedicine, Academy of Finland grant, 2018-2019
- MACOME - Machine Learning for Computational Metabolomics, Academy of Finland grant, 2017-2021
- FCHealth - Foundations of Computational Health, HIIT research programme
- CS-E3210 Machine Learning: Basic Principles, I, period/Autumn 2019, Juho Rousu/Maryam Sabzevari
- CS-E4830 Kernel Methods in Machine Learning, III-IV. period/Spring 2020, Rohit Babbar
Check out the CSI:FingerID server for metabolite identification from MS/MS data, running the methods we developed with Sebastian Boecker's group in Friedrich-Schiller-Universitat Jena.
You may also check the clip from YLE Popular science program Prisma Studio (In Finnish only!), showcasing how metabolites are identified using machine learning in the CSI:FingerID search engine. See the clip here! (.mp4, 50MB
Please find our software page here and our GitHub page at github.com/aalto-ics-kepaco.
Selected and recent publications
- Uurtio, V., 2020. Methods for Interpreting Kernel Canonical Correlation Measures. Aalto University publication series DOCTORAL DISSERTATIONS, 21/2020
- Brouard, C., Basse, A., d'Alche-Buc, F. and Rousu, J., 2019. Improved Small Molecule Identification through Learning Combinations of Kernel Regression Models. Metabolites, 9(8), p.160.
- Heinonen, M., Osmala, M., Mannerstrom, H., Wallenius, J., Kaski, S., Rousu, J. and Lahdesmaki, H., 2019. Bayesian metabolic flux analysis reveals intracellular flux couplings. Bioinformatics, 35(14), pp.i548-i557.
- Uurtio, V., Bhadra, S. and Rousu, J., 2019, May. Large-Scale Sparse Kernel Canonical Correlation Analysis. In International Conference on Machine Learning (pp. 6383-6391).
- Duehrkop, K., Fleischauer, M., Ludwig, M., Aksenov, A.A., Melnik, A.V., Meusel, M., Dorrestein, P.C., Rousu, J. and Böcker, S., 2019. SIRIUS 4: a rapid tool for turning tandem mass spectra into metabolite structure information. Nature methods, 16(4), p.299.
- Uurtio, V., Bhadra, S. and Rousu, J., 2018, November. Sparse non-linear cca through hilbert-schmidt independence criterion. In 2018 IEEE International Conference on Data Mining (ICDM) (pp. 1278-1283). IEEE.
- Cichonska, A., 2018. Machine Learning for Systems Pharmacology. Aalto University publication series DOCTORAL DISSERTATIONS; 168/2018. Link to publication
- Bach, E., Szedmak, S., Brouard, C., Boecker, S. and Rousu, J., 2018. Liquid-chromatography retention order prediction for metabolite identification. Bioinformatics, 34(17), pp.i875-i883.
- Cichonska, A., Pahikkala, T., Szedmak, S., Julkunen, H., Airola, A., Heinonen, M., Aittokallio, T. and Rousu, J., 2018. Learning with multiple pairwise kernels for drug bioactivity prediction. Bioinformatics, 34(13), pp.i509-i518.
- Bhadra, S., Blomberg, P., Castillo, S. and Rousu, J., 2018. Principal metabolic flux mode analysis. Bioinformatics, 34(14), pp.2409-2417.
- Uurtio, V., Monteiro, J.M., Kandola, J., Shawe-Taylor, J., Fernandez-Reyes, D. and Rousu, J., 2018. A tutorial on canonical correlation methods. ACM Computing Surveys (CSUR), 50(6), p.95.
- Bhadra, S., Kaski, S. and Rousu, J., 2017. Multi-view kernel completion. Machine Learning, 106(5), pp.713-739.
- Cichonska, A., Ravikumar, B., Parri, E., Timonen, S., Pahikkala, T., Airola, A., Wennerberg, K., Rousu, J. and Aittokallio, T., 2017. Computational-experimental approach to drug-target interaction mapping: A case study on kinase inhibitors. PLOS Computational Biology, 13(8), p.e1005678.
- Schymanski, E.L., Ruttkies, C., Krauss, M., Brouard, C., Kind, T., Dührkop, K., Allen, F., Vaniya, A., Verdegem, D., Boecker, S. and Rousu, J., 2017. Critical assessment of small molecule identification 2016: automated methods. Journal of cheminformatics, 9(1), p.22.
- Brouard, C., Shen, H., Dührkop, K., d'Alché-Buc, F., Böcker, S. and Rousu, J., 2016. Fast metabolite identification with input output kernel regression. Bioinformatics, 32(12), pp.i28-i36.
- Cichonska, A., Rousu, J., Marttinen, P., Kangas, A.J., Soininen, P., Lehtimäki, T., Raitakari, O.T., Järvelin, M.R., Salomaa, V., Ala-Korpela, M. and Ripatti, S., 2016. metaCCA: summary statistics-based multivariate meta-analysis of genome-wide association studies using canonical correlation analysis. Bioinformatics, 32(13), pp.1981-1989.
- Kai Duehrkop , Huibin Shen, Marvin Meusel, Juho Rousu, and Sebastian Boecker. Searching molecular structure databases with tandem mass spectra using CSI:FingerID. Proceedings of the National Academy of Sciences, vol. 112, 41 (2015) pp. 12580-12585
Visitors to the group
- 2020: Prof. Cecile Capponi, Aix-Marseille Universite
- 2019: Prof. Sahely Bhadra, IIT Palakkad, India, Dr. Kai Dührkop, Friedrich-Schiller University Jena
- 2018: Dr. Christina Leslie, Memorial Sloan Kettering Cancer Center, USA
- 2017: Prof. Francois Laviolette, Laval University, Canada; Prof. Sebastian Boecker, Friedrich-Schiller University of Jena
- 2016: Prof. Sebastian Boecker, Dr. Tim White, Marcus Ludwig, Kai Duehrkop, Friedrich-Schiller University Jena
- 2015: Prof. Giorgio Valentini, Universita Degli Studi di Milan
- 2013: Prof. Sebastian Boecker, Friedrich-Schiller University Jena
- Dr. Ari Rantanen, PhD 2006, currently at Tieto Corp (LinkedIn)
- Dr. Esa Pitkänen, PhD 2010, currently at FIMM (LinkedIn)
- Dr. Markus Heinonen, PhD 2013, currently at CSB group, Aalto University (LinkedIn)
- Dr. Jefrey Lijffijt, PhD 2013 (LinkedIn)
- Dr. Jana Kludas, post-doc 2012-2015 (ResearchGate)
- Dr. Hongyu Su, PhD 2015, now at Nordea Bank (LinkedIn)
- Dr. Elena Czeizler, research fellow, 2013-16, at Varian Medical Systems (LinkedIn )
- Dr. Sahely Bhadra, post-doc, 2014-2016, now Assistant professor at IIT Palakkad (LinkedIn)
- Dr. Huibin Shen, PhD 2017, now at Amazon Berlin (LinkedIn)
- Dr. Celine Brouard, postdoc 2014-2018, moved to INRA Tolouse (ResearchGate/li>
- Dr. Anna Cichonska, PhD 2018, moved to Nightingale Health (ResearchGate)
Former students and interns
- Yvonne Herrmann, MSc 2012 (LinkedIn)
- Fitsum Tamene, MSc 2013 (LinkedIn)
- Jian Hou, MSc 2014
- Iitu Kuittinen, MSc 2015 (LinkedIn)
- Nicole Althermeler, MSc 2016 (LinkedIn)
- Jinmin Lei, MSc 2016
- Maja Ilievska, MSc 2016 (LinkedIn)
- Mohamed Jabri, MSc 2017 (LinkedIn)
- Linh Nguyen, MSc 2017 (LinkedIn)
- Parisa Mapar, MSc 2018 (LinkedIn)
- Tolou Shadbahr, MSc 2019
- Vilma Jägerroos, MSc 2019
- Heli Julkunen, MSc 2019
- Clemens Westrup, intern 2013-15 (LinkedIn)
- Carlos Maycas Nadal, BSc 2014 (LinkedIn)
- Anton Mattsson, intern 2017 (LinkedIn)
- Zheyang Shen, research assistant 2017
- Fabio Colella, research assistant 2018
- Antoine Basse, intern 2018 (with Telecom ParisTech)
- Jane Douat, intern 2019
- Bugra Aker Yilmaz, intern 2019
- Antoine Moulin, intern 2019