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. Machine learning applications of interest include metabolite identification, metabolic network reconstruction and pathway analysis, chemogenomics as well as biomarker discovery.
See overview of KEPACO research (in PDF)
- KEPACoffee, regular group gathering
- November 17--20,2018. Viivi presents her paper "Sparse Non-linear CCA through Hilbert-Schmidt Independence Criterion" at the ICDM-2018 conference at Singapore.
- October 8--December 21, 2018. Juho is visiting Telecom Paristech
- October 5, 2018. Anna Cichonska defends her PhD thesis "Machine Learning for Systems Pharmacology"
- October 4--5, 2018. Dr. Christina Leslie visits the group, giving a guest seminar and acting as the opponent of the PhD defence of Anna.
- September 8--12, 2018. Eric presents his paper "Liquid-chromatography retention order prediction for metabolite identification" at ECCB 2018
- August 20-22, 2018. Heli participates in the Summer School on Machine Learning in Drug Design, Leuven, Belgium
- August 13-September 21, 2018. Juho visits Friedrich-Schiller Universitat Jena, hosted by Prof Sebastian Boecker.
- July 8, 2018: Anna received Best Oral Presentation award of the TransMed COSI at ISMB 2018. Congrats Anna!
- July 6-10, 2018: Anna presents her paper "Learning with multiple pairwise kernels
for drug bioactivity prediction" at ISMB-2018, Chicago.
- July 4-5, 2018: Juho and Anna visit Universite Laval in Quebec City, Canada
- June 28, 2018: KEPACO Summer retreat takes place in the wonderful Hvittrask
- June 4, 2018: Juho gives an invited presentation "Machine Learning for Small Molecule Identification" at the Annual Symposium on Computer Science in Finland 2018.
- May 11, 2018: KEPACO Alumnus Dr. Huibin Shen won the award for the best Finnish bioinformatics Ph.D. thesis done in 2016-2017 by Finnish Bioinformatics Society in their Annual meeting. Congratulations Huibin!
- May 2, 2018: Vilma Jägerroos starts as a intern with KEPACO in collaboration with Orion. Welcome Vilma!
- April 23, 2018: Antoine Basse from Telecom ParisTech starts as an intern. Welcome Antoine!
- April 3-6, 2018: Juho visits University College London
- March 8, 2018: Several KEPACO members participate and co-organize the Women in Data Science seminar in Helsinki
- February 1, 2018: Fabio Colella starts as a Honours student in our group. Welcome Fabio!
- June 6, 2017: See the Helsinki Challenge semi-final pitch of our Metabold team on Youtube here. The pitch starts at 52:28.
- March 30, 2017: Huibin Shen defends his PhD thesis "Machine Learning for Small Molecule Identification".
The opponent is Prof. Francois Laviolette.
- January 16, 2017: We got to the semi-final of Helsinki Challenge with our joint team with University of Eastern Finland, lead by Dr. Kati Hanhineva. 20 ouf of 110 teams were selected for the semi-final.
- September 3-4, 2016: KEPACO Co-organize the 10th International Workshop on Machine Learning in Systems Biology (MLSB 2016), in conjunction with ECCB 2016 conference in The Hague, The Netherlands.
- May 19-20, 2016: Anna Cichonska and Viivi Uurtio co-organize the Bioinformatics Research and Education Workshop (BREW) in Helsinki
- May 6, 2016: KEPACO did well in the CASMI 2016 contest, Team Brouard (KEPACO/Jena) winning one category and Team Duehrkop (Jena/KEPACO) coming second.
- November 29 -- December 4, 2015: KEPACO co-organized Dagstuhl workshop Computational Metabolomics with ca. 30 international top experts
- November 4, 2015: Juho Rousu appeared in YLE Popular science program Prisma Studio (In Finnish only!), explaing how metabolites are identified using machine learning in the CSI:FingerID search engine. See the clip here! (.mp4, 50MB
- Older news
- Juho Rousu, Professor, group leader
- Rohit Babbar, Assistant professor
- Sandor Szedmak, PhD, senior research scientist
- Maryam Sabzevari, PhD, post-doctoral researcher
- Anna Cichonska, post-doctoral researcher (FIMM)
- Viivi Uurtio, PhD student
- Eric Bach, PhD student
- Tolou Shadbahr, MSc student
- Heli Julkunen, MSc student
- Vilma Jägerroos, 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
- 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
- D4Health - Data-Driven Decision Support for Digital Health. Academy of Finland grant 2016-2018.
- LiF - Living Factories. Finnish Funding Agency for Innovation, 2014-2017
- MIDAS - Metabolite Identification through Algorithms and Statistical Learning. Academy of Finland grant 2013-2017
- CS-E4830 Kernel Methods in Machine Learning, I-II. period/Autumn 2017, Juho Rousu/Celine Brouard/Sandor Szedmak/Parisa Mapar
- CS-E4880 Machine Learning in Bioinformatics, I-II peroid/Autumn 2017, Juho Rousu/Viivi Uurtio/Anna Cichonska/Eric Bach/and others
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
- Kai Duehrkop, Markus Fleischauer, Marcus Ludwig, Alexander A. Aksenov, Alexey V. Melnik, Marvin Meusel, Pieter C. Dorrestein, Juho Rousu, and Sebastian Boecker. SIRIUS 4: Turning tandem mass spectra into metabolite structure information. Nature Methods, 2018, in press
- Uurtio, V., Bhadra, S., Rousu, J. Sparse Non-Linear CCA through
Hilbert-Schmidt Independence Criterion. IEEE International Conference on Data Mining (ICDM 2018), to appear
- 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, Advance access, https://doi.org/10.1093/bioinformatics/bty049
- Uurtio, V., Monteiro, J.M., Kandola, J., Shawe-Taylor, J., Fernandez-Reyes, D. and Rousu, J., 2017. A Tutorial on Canonical Correlation Methods. ACM Computing Surveys (CSUR), 50(6), p.95.
- Brouard, C., Bach, E., Böcker, S. and Rousu, J., 2017, November. Magnitude-Preserving Ranking for Structured Outputs. In Asian Conference on Machine Learning (pp. 407-422).
- 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., Duehrkop, K., Allen, F., Vaniya, A., Verdegem, D., Boecker, S. and Rousu, J. et al. 2017. Critical assessment of small molecule identification 2016: automated methods. Journal of cheminformatics, 9(1), p.22.
- Shen, H 2017, Machine Learning for Small Molecule Identification. Aalto University publication series DOCTORAL DISSERTATIONS, no. 25, vol. 2017, Aalto University. Link to publication
- Huibin Shen, Sandor Szedmak, Celine Brouard, Juho Rousu. Soft Kernel Target Alignment for Two-Stage Multiple Kernel Learning. Proc. 19th International Conference on Discovery Science (DS 2016), 2016, to appear
- Jana Kludas, Mikko Arvas, Sandra Castillo, Tiina Pakula, Merja Oja, Celine Brouard, Jussi Jantti, Merja Penttila, Juho Rousu. Machine learning of protein interactions in fungal secretory pathways. PLOS One, 2016, to appear
- Celine Brouard, Huibin Shen, Kai Duehrkop, Florence D'Alche-buc, Sebastian Boecker and Juho Rousu. Fast metabolite identification with Input Output Kernel Regression. Proc. Intelligent Systems for Molecular Biology, ISMB 2016, Bioinformatics, 2016, to appear
- Markus Heinonen, Henrik Mannerstrom, Juho Rousu, Samuel Kaski, Harri Lahdesmaki. Non-Stationary Gaussian Process Regression with Hamiltonian Monte Carlo. The 19th International Conference on Articial Intelligence and Statistics, AISTATS 2016, Journal of Machine Learning Research Workshop and Conference Proceedings, 2016, to appear
- Honeyborne I, McHugh TD, Kuittinen I, Cichonska A, Evangelopoulos D, Ronacher K, van Helden PD, Gillespie SH, Fernandez-Reyes D, Walzl G, Rousu J. Profiling persistent tubercule bacilli from patient sputa during therapy predicts early drug efficacy. BMC medicine. 2016 Apr 7;14(1):1.
- Anssi Rantasalo, Elena Czeizler, Riitta Virtanen, Juho Rousu, Harri Lahdesmaki, Merja Penttila, Jussi Jantti, Dominik Mojzita. Synthetic Transcription Amplifier System for Orthogonal Control of Gene Expression in Saccharomyces cerevisiae. PLOS One, 2016, doi:10.1371/journal.pone.0148320
- Anna Cichonska, Juho Rousu, Pekka Marttinen, Antti J Kangas, Pasi Soininen, Terho Lehtimaki, Olli T Raitakari, Marjo-Riitta Jarvelin, Veikko Salomaa, Mika Ala-Korpela, Samuli Ripatti, Matti Pirinen. metaCCA: Summary statistics-based multivariate meta-analysis of genome-wide association studies using canonical correlation analysis. Bioinformatics, Advance Access February 2016.
- 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
- Hongyu Su, Juho Rousu. Multilabel classification through random graph ensembles. Machine Learning, Volume 99, Issue 2 (2015), pp 231-256 http://dx.doi.org/10.1007/s10994-014-5465-9.
- Hongyu Su. Multilabel Classification through Structured Output Learning - Methods and Applications. PhD thesis, Aalto university, 2015
Visitors to the group
- 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 Sanoma Corp (LinkedIn)
- Dr. Esa Pitkänen, PhD 2010, currently at EMBL (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 Pakkalad (LinkedIn)
- Dr. Huibin Shen, PhD 2017, now at Amazon Berlin (LinkedIn)
- Dr. Celine Brouard, moved to INRA Tolouse (ResearchGate/li>
- 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)
- 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)