Department information

Juho Rousu

Teaching

• Spring 2010: Data mining, Data mining project

Research interests

• Design and analysis of algorithms
• Machine learning, kernel methods
• Kernels for structured domains
• Machine learning for text
• Computational methods in systems biology
• Metabolic networks, flux estimation
• Functional genomics, protein function prediction

Activities

• Computational Systems Biology and Bioinformatics research group
• Machine Learning in Systems Biology (MLSB-2009)
• International Conference in Machine Learning
• PASCAL Network of Excellence
• Algorithmic data analysis National Centre of Excellence

Selected & Recent Publications

• Saso Dzeroski, Pierre Geurts, Juho Rousu. Proceedings of the Third International Workshop on Machine Learning in Systems Biology: Revised Selected Papers. Journal of Machine Learning Research Workshop and Conference Proceedings 8:1-2, 2010
• Markus Heinonen, Sampsa Lappalainen, Taneli Mielikäinen, Juho Rousu. Computing Atom Mappings for Biochemical Reactions without Subgraph Isomorphism. Journal of Computational Biology, 2010, to appear.
• Esa Pitkänen, Juho Rousu, Esko Ukkonen: Computational methods for metabolic reconstruction. Current Opinion in Biotechnology, 2010.
• Katja Astikainen, Liisa Holm, Esa Pitkanen, Sandor Szedmak, Juho Rousu. Reaction kernels: structured output prediction approaches for novel enzyme function. 1st International Conference on Bioinformatics, Valencia, Spain, January 2010.
• Esa Pitkanen, Paula Jouhten, Juho Rousu. Inferring Branching Pathways in Genome-Scale Metabolic Networks. BMC Systems Biology 2009, 3:103
• Sandor Szedmak, Craig Saunders, Yizhao Ni and Juho Rousu. Max-margin structured output learning in L1 norm space, PASCAL Technical reports, 2009
• Saso Dzeroski, Pierre Geurts, Juho Rousu (Eds.). Machine Learning in Systems Biology. Proc. 3rd International Workshop, Ljubljana, Slovenia, September 5-6, 2009. Report B-2009-1, Department of Computer Science, University of Helsinki
• Juho Rousu. Bioinformatics: Technologies and Challenges. Proceedings of AMICT 2008, Advances in Methods of Information and Communication Technology. Petrozavodsk State University, 2009, 50--57
• Katja Astikainen, Liisa Holm, Esa Pitkänen, Sandor Szedmak, Juho Rousu. Towards Structured Output Prediction of Enzyme Function . BMC Proceedings 2, Suppl 4 (2008):S2
• Markus Heinonen, Ari Rantanen, Taneli Mielikäinen, Juha Kokkonen, Jari Kiuru, Raimo A. Ketola, Juho Rousu: FiD: a software for ab initio structural identification of product ions from tandem mass spectrometric data. Rapid Communications in Mass Spectrometry 22, 19 (2008), 3043 - 3052
• Ari Rantanen, Juho Rousu, Paula Jouhten, Nicola Zamboni, Hannu Maaheimo, Esko Ukkonen. An analytic and systematic framework for estimating metabolic flux ratios from 13C tracer experiments. BMC Bioinformatics 2008, 9:266.
• Esa Pitkänen, Ari Rantanen, Juho Rousu, Esko Ukkonen. A computational method for reconstructing gapless metabolic networks. Proc. 2nd International Conference on Bioinformatics Research and Development (BIRD '08). Communications in Computer and Information Science, Vol. 13, Springer, 2008
• J. Rissanen, P. Grunwald, J. Heikkonen, P. Myllymäki, T Roos, Juho Rousu (eds.). Information Theoretic Methods for Bioinformatics. EURASIP Journal on Bioinformatics and Systems Biology, vol. 2007, (2007)
• Samuel Kaski, Esko Ukkonen, Juho Rousu (eds.) Probabilistic modeling and machine learning in structural and systems biology . PMSB 2006 special issue. BMC Bioinformatics 2007, 8 (Suppl 2), S1
• Huizhen Yu, Juho Rousu. An Efficient Method for Large Margin Parameter Optimization in Structured Prediction Problems , Technical report C-2007-87, Dept. Computer Science, Univ. of Helsinki, 2007.
• Juho Rousu, Craig Saunders, Sandor Szedmak, John Shawe-Taylor. Efficient algorithms for max-margin structured classification. In G. Bakir, T. Hofmann, B. Schölkopf, A. Smola, B. Taskar, S.V.N Vishwanathan (eds.): Predicting Structured Data, MIT Press, 2007
• Juho Rousu, Craig Saunders, Sandor Szedmak and John Shawe-Taylor. Kernel-based Learning of Hierarchical Multilabel Classification Models . Journal of Machine Learning Research 7 (2006), pp. 1601 - 1626. MATLAB implementation of the learning algorithm is available here.
• Juho Rousu, John Shawe-Taylor. Efficient Computation of Gapped Substring Kernels on Large Alphabets. Journal of Machine Learning Research 6 (2005), 1323-1344. MATLAB Implementation of the sparse dynamic programming algorithm
• Juho Rousu, Craig Saunders, Sandor Szedmak, John Shawe-Taylor. Learning Hierarchical Multi-Category Text Classification Models . 22nd International Conference on Machine Learning (ICML'2005), 745--752
• Tapio Elomaa, Juho Rousu. Efficient multisplitting revisited: Optima-preserving elimination of partition candidates. Data Mining and Knowledge Discovery 8, 2 (2004), 97-126.
• Juho Rousu. General and Efficient Multisplitting of Numerical Attributes (ps.gz, 184kb) . Machine Learning 36, 3 (1999), pp. 201-244. (c) 1999 Kluwer Academic Publishers

A more complete publication list