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Kernel Methods, Pattern Analysis and Computational Biology (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)

News

  • Older news

    Personnel and contact information

    • Juho Rousu, Professor, group leader
    • Sandor Szedmak, PhD, senior research scientist
    • Taneli Pusa, PhD, post-doctoral researcher (HIIT)
    • Maryam Sabzevari, PhD, post-doctoral researcher
    • Riikka Huusari, PhD, post-doctoral researcher
    • Eric Bach, PhD student
    • Tianduanyi Wang, PhD student (FIMM/HIIT)
    • Gianmarco Midena, PhD student
    • Robert Armah-Sekum, PhD student
    • Luc Motte, Visiting PhD student (Telecom Paris)

    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

    Activities

    Research projects

    • MASF - Machine Learning for Systems Pharmacology. Academy of Finland grant 2021-2025
    • AIB - Artificial intelligence for interaction prediction in biomedicine, Academy of finland grant 2022-2024
    • MAGITICS - MAchine learning for diGItal diagnosTICS of antimicrobial resistance, JPI/Academy of Finland grant, 2020-2023

    Teaching 2021-23

    • CS-E4710 Machine Learning: Supervised Methods, Autumn 2022
    • CS-E4880 Machine Learning in Bioinformatics, Spring 2023

    Software

    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.

    Please find our software in GitHub page github.com/aalto-ics-kepaco.

    Publications

    Selected and recent publications

    • Wang, T., Szedmak, S., Wang, H., Aittokallio, T., Pahikkala, T., Cichonska, A. and Rousu, J., 2021. Modeling drug combination effects via latent tensor reconstruction. Bioinformatics 37 (Supplement_1), i93-i101
    • Huusari, R., Bhadra, S., Capponi, C., Kadri, H. and Rousu, J., 2021. Learning primal-dual sparse kernel machines. arXiv preprint arXiv:2108.12199
    • Bach, E., Rogers, S., Williamson, J. and Rousu, J., 2021. Probabilistic framework for integration of mass spectrum and retention time information in small molecule identification. Bioinformatics, 37(12), pp.1724-1731.
    • Duehrkop, K., Nothias, L.F., Fleischauer, M., Reher, R., Ludwig, M., Hoffmann, M.A., Petras, D., Gerwick, W.H., Rousu, J., Dorrestein, P.C. and Böcker, S., 2021. Systematic classification of unknown metabolites using high-resolution fragmentation mass spectra. Nature Biotechnology, 39(4), pp.462-471. https://doi.org/10.1038/s41587-020-0740-8
    • Hjoerleifsson Eldjárn, G., Ramsay, A., Van Der Hooft, J.J., Duncan, K.R., Soldatou, S., Rousu, J., Daly, R., Wandy, J. and Rogers, S., 2021. Ranking microbial metabolomic and genomic links in the NPLinker framework using complementary scoring functions. PLoS computational biology, 17(5), p.e1008920.
    • Wang, T., Szedmak, S., Wang, H., Aittokallio, T., Pahikkala, T., Cichonska, A., Rousu, J., 2021. Modeling drug combination effects via latent tensor reconstruction. Proc. ISMB/ECCB'21. Bioinformatics 37, Suppl 1, pp. i93-i101
    • Julkunen, H., Cichonska, A., Gautam, P., Szedmak, S., Douat, J., Pahikkala, T., Aittokallio, T. and Rousu, J., 2020.} Leveraging multi-way interactions for systematic prediction of pre-clinical drug combination effects. Nature communications, 11(1), p.6136.
    • Voutilainen, S., Heinonen, M., Andberg, M., Jokinen, E., Maaheimo, H., Paakkonen, J., Hakulinen, N., Rouvinen, J., Lahdesmaki, H., Kaski, S. and Rousu, J., 2020. Substrate specificity of 2-deoxy-D-ribose 5-phosphate aldolase (DERA) assessed by different protein engineering and machine learning methods. Applied Microbiology and Biotechnology, 104(24), p.10515.
    • Wang, T., Gautam, P., Rousu, J. and Aittokallio, T., 2020. Systematic mapping of cancer cell target dependencies using high-throughput drug screening in triple-negative breast cancer. Computational and structural biotechnology journal, 18, p.3819.
    • Brogat-Motte, L., Rudi, A., Brouard, C., Rousu, J. and d'Alché-Buc, F., 2020. Learning Output Embeddings in Structured Prediction. arXiv preprint arXiv:2007.14703.
    • 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.
    • 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., Duhrkop, 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 and Alumni

    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

    Alumni

    • 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)
    • Dr. Viivi Uurtio, PhD 2020, now data scientist at Elisa (Linkedin)

    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
    • Santeri Mentu, MSc 2020
    • Minna Oksanen, MSc 2021
    • Amandine Grosfils, MSc 2021
    • 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
    • Wen Xiang, intern 2020