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)
- May 15 - July 17, 2023. Indumathi P from Indian Institute Of Technology (IIT) Madras visits KEPACO. Welcome Indumathi!
- May 8-12, 2023. Juho co-organizes the Computational metabolomics and machine learning workshop in Shonan, Japan.
- April 3 - July 22, 2023. Elina Francovic-Fontaine from Laval University visits KEPACO. Welcome Elina!
- March 7 - May 30, 2023. Golsar Fatemeh Abbasi from University of Tehran visits KEPACO. Welcome Golsar!
- February 27 - June 1, 2023. Prof. Elena Casiraghi from University of Milan visits KEPACO. Welcome back Elena!
- February 20 - March 1, 2023. Taneli visits Marie-France Sagot's group, at Inria, University of Lyon 1, France.
- January 13, 2023. Eric Bach defends his PhD thesis Machine learning methods for structural elucidation in untargeted metabolomics
- January 9, 2023. Ellimari Paunio starts her MSc thesis in KEPACO group. Welcome Ellimari!
- November 17, 2022. Taneli chaired the WASP meeting "AI in biology and healthcare".
- November 14-18, 2022. Prof. Elena Casiraghi from University of Milan visits KEPACO. Welcome back Elena!
- October 2-12, 2022. Taneli visits Marie-France Sagot's group, at Inria, University of Lyon 1, France.
- September 26-30, 2022. Prof. Elena Casiraghi from University of Milan visits KEPACO. Welcome Elena!
- August 15-26, 2022. Riikka and Juho visit Laval University, Quebec City.
- June 28, 2022. KEPACO summer retreat takes place at Suomenlinna island.
- May 16-20, 2022. Riikka visits the Qarma group, at Aix-Marseille University, France.
- May 2, 2022. Maryam Astero starts as a PhD student in KEPACO. Welcome Maryam!
- January 12, 2022. Robert Armah-Sekum starts as a PhD student in KEPACO. Welcome Robert!
Older news
- Juho Rousu, Professor, group leader
- Sandor Szedmak, PhD, senior research scientist
- Taneli Pusa, PhD, post-doctoral researcher (HIIT)
- Riikka Huusari, PhD, post-doctoral researcher
- Tianduanyi Wang, PhD student (FIMM/HIIT)
- Gianmarco Midena, PhD student
- Robert Armah-Sekum, PhD student
- Maryam Astero, PhD student
- Ellimari Paunio, MSc student
- Elina Francovic-Fontaine, Visiting PhD student (Laval University)
- Indumathi P, Visiting PhD student (IIT Madras)
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
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
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.
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 to the group
- 2023: Prof. Elena Casiraghi, Università degli Studi di Milano
- 2023: Golsar Fatemeh Abbasi, PhD student, University of Tehran
- 2020: Prof. Cecile Capponi, Aix-Marseille Universite
- 2019: Prof. Sahely Bhadra, IIT Palakkad, India, Dr. Kai Dührkop, Friedrich-Schiller University Jena
- 2019: Dr. Luc Motte, Telecom Paris
- 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, Università degli Studi di Milano
- 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)
- Dr. Anna Cichonska, PhD 2018, moved to Nightingale Health (ResearchGate)
- Dr. Viivi Uurtio, PhD 2020, now data scientist at Elisa (Linkedin)
- Dr. Maryam Sabzevari, post-doc, 2018-2022, now at Nokia Bell Labs (Linkedin)
- Dr. Eric Bach, PhD 2023, 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
- Amandine Grosfils, MSc 2021
- Luc Motte, PhD 2023, Telecom Paris