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
- March 4 - June 7, 2024. Prof. Elena Casiraghi from University of Milan visits KEPACO. Welcome back Elena!
- November 15, 2023. Dr. Emily Bennett from VTT starts as a visiting post-doctoral researcher in biodesign at KEPACO group. Welcome Emily!
- November 11-14, 2023. Riikka presents our paper "Scalable Variable Selection for Two-View Learning Tasks with Projection Operators" at ACML 2023 conference.
- October 14-21, 2023. Taneli and Juho visit IIT Palakkad and IIT Madras.
- September 26 - October 5, 2023. Taneli visits Marie-France Sagot and the Erable group, at Inria, University of Lyon 1, France.
- September 12, 2023. Dr. Anchen Li starts as a post-doctoral researcher in KEPACO group. Welcome Anchen!
- September 11, 2023. Heli Julkunen starts as a PhD student in KEPACO. Welcome back Heli!
- June 5-9, 2023. Taneli visits Marie-France Sagot and the Erable group, at Inria, University of Lyon 1, France.
- May 15 - July 14, 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 20, 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 and the Erable 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!
Personnel and contact information
- Juho Rousu, Professor, group leader
- Sandor Szedmak, PhD, senior research scientist
- 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
- Heli Julkunen, PhD student
- Anchen Li, PhD, post-doctoral researcher
- Emily Bennett, PhD, post-doctoral researcher (VTT)
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
- Biodesign - Virtual Laboratory for Enzyme Design, Jane and Aatos Erkko Foundation grant 2023-2027
- 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
Please find our software in GitHub page github.com/aalto-ics-kepaco.
Publications
Selected and recent publications
- Li, A., Casiraghi, E. and Rousu, J., 2024. Chemical Reaction Enhanced Graph Learning for Molecule Representation. Bioinformatics, p.btae558.
- Pusa, T. and Rousu, J., 2024. Stable biomarker discovery in multi-omics data via canonical correlation analysis. PloS one, 19(9), p.e0309921.
- Schulman, A., Rousu, J., Aittokallio, T. and Tanoli, Z., 2024. Attention-based approach to predict drug-target interactions across seven target superfamilies. Bioinformatics, 40(8), p.btae496.
- Abbasi, F. and Rousu, J., 2024. New methods for drug synergy prediction: A mini-review. Current Opinion in Structural Biology, 86, p.102827.
- Armah-Sekum, R.E., Szedmak, S. and Rousu, J., 2024. Protein function prediction through multi-view multi-label latent tensor reconstruction. BMC bioinformatics, 25(1), p.174.
- Astero, M. and Rousu, J., 2024. Learning symmetry-aware atom mapping in chemical reactions through deep graph matching. Journal of Cheminformatics, 16(1), p.46.
- Szedmak, S., Huusari, R., Duong Le, T.H. and Rousu, J., 2023. Scalable variable selection for two-view learning tasks with projection operators. Machine Learning, pp.1-20.
- Sandström, H., Rissanen, M., Rousu, J. and Rinke, P., 2024. Data-Driven Compound Identification in Atmospheric Mass Spectrometry. Advanced Science, 11(8), p.2306235.
- Bach, E., 2022. Machine learning methods for structural elucidation in untargeted metabolomics. PhD thesis. Alto University.
- Bach, E., Schymanski, E.L. and Rousu, J., 2022. Joint structural annotation of small molecules using liquid chromatography retention order and tandem mass spectrometry data. Nature Machine Intelligence, 4(12), pp.1224-1237.
- Brogat-Motte, L., Flamary, R., Brouard, C., Rousu, J. and d'Alché-Buc, F., 2022, June. Learning to predict graphs with fused Gromov-Wasserstein barycenters. In International Conference on Machine Learning (pp. 2321-2335). PMLR.
- Sabzevari, M., Szedmak, S., Penttilä, M., Jouhten, P. and Rousu, J., 2022. Strain design optimization using reinforcement learning. PLoS computational biology, 18(6), p.e1010177.
- Brogat-Motte, L., Rudi, A., Brouard, C. and Rousu, J., 2022. Vector-valued least-squares regression under output regularity assumptions. The Journal of Machine Learning Research, 23(1), pp.15608-15657.
- Kong, W., Midena, G., Chen, Y., Athanasiadis, P., Wang, T., Rousu, J., He, L. and Aittokallio, T., 2022. Systematic review of computational methods for drug combination prediction. Computational and Structural Biotechnology Journal, 20, pp.2807-2814.
- 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
Visitors and Alumni
Visitors to the group
- 2023-24: Prof. Elena Casiraghi, Università degli Studi di Milano
- 2023: Elina Francovic-Fontaine, PhD student, Laval University
- 2023: Indumathi P, PhD student, IIT Madras
- 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. Taneli Pusa, post-doc 2021-2024
- Dr. Eric Bach, PhD 2023 (next: data scientist at Elisa)
- Dr. Maryam Sabzevari, post-doc 2018-2022 (next: machine learning research scientist at Nokia Bell Labs)
- Dr. Viivi Uurtio, PhD 2020 (next: data scientist at Elisa)
- Dr. Anna Cichonska, PhD 2018 (next: postdoctoral researcher at FIMM - University of Helsinki and senior data scientist at Nightingale Health)
- Dr. Celine Brouard, post-doc 2014-2018 (next: researcher at INRA Tolouse)
- Dr. Huibin Shen, PhD 2017 (next: machine learning scientist at Amazon Berlin)
- Dr. Sahely Bhadra, post-doc 2014-2016 (next: assistant professor at IIT Palakkad)
- Dr. Elena Czeizler, research fellow 2013-16 (next: research scientist at Varian Medical Systems)
- Dr. Hongyu Su, PhD 2015 (next: lead machine learning engineer at Nordea Bank)
- Dr. Jana Kludas, post-doc 2012-2015 (next: lead data scientist at msg)
- Dr. Jefrey Lijffijt, PhD 2013 (next: research associate in data science at University of Bristol)
- Dr. Markus Heinonen, PhD 2013 (next: postdoctoral researcher at Universite d'Evry-Val d'Essonne)
- Dr. Esa Pitkänen, PhD 2010 (next: postdoctoral researcher at University of Helsinki)
- Dr. Ari Rantanen, PhD 2006 (next: data scientist at Tieto Corp)
Former students and interns
- Ellimari Paunio, MSc 2023 (next: Junior Bioinformatician at CSC - IT Center for Science)
- Luc Motte, PhD 2023, Telecom Paris
- Amandine Grosfils, MSc 2021
- Minna Oksanen, MSc 2021
- Wen Xiang, intern 2020
- Santeri Mentu, MSc 2020
- Antoine Moulin, intern 2019
- Bugra Aker Yilmaz, intern 2019
- Jane Douat, intern 2019
- Heli Julkunen, MSc 2019
- Vilma Jägerroos, MSc 2019
- Tolou Shadbahr, MSc 2019
- Antoine Basse, intern 2018 (with Telecom ParisTech)
- Fabio Colella, research assistant 2018
- Parisa Mapar, MSc 2018
- Zheyang Shen, research assistant 2017
- Anton Mattsson, intern 2017
- Linh Nguyen, MSc 2017
- Mohamed Jabri, MSc 2017
- Jinmin Lei, MSc 2016
- Maja Ilievska, MSc 2016
- Nicole Althermeler, MSc 2016
- Iitu Kuittinen, MSc 2015
- Clemens Westrup, intern 2013-15
- Jian Hou, MSc 2014
- Carlos Maycas Nadal, BSc 2014
- Fitsum Tamene, MSc 2013
- Yvonne Herrmann, MSc 2012