Kernel Machines, Pattern Analysis, and Computational Biology

The group is part of HIIT since beginning of 2012. The group develops machine learning methods, models and tools for computational sciences, in particularcomputational biology. The methodological backbone of the group is kernel methods and regularized learning. The group particularly focusses in learning with multiple and structured targets, multiple views and ensembles. Applications of interest in computational biology include network reconstruction, gene functional classification as well as biomarker discovery.

Where the find us

We are located at the ICS department of Aalto University. 

News

• Research fellow in Bioinformatics and Systems Biology (DL Feb 28,2013)
• Summer internship in Protein-Protein Interaction prediction (DL Jan 31, 2013)

Activities

• BIOLEDGE - BIO knowLEDGE Extractor and Modeller for Protein Production. EU FP7 STREP (2011-2016).
• GEOBIOINFO - Deep biosphere bioinformatics. Part of KYT2014 programme funded by Ministry of Employment and the Economy
• Algorithmic Data Analysis Academy of Finland National Center of Excellence.
• EU FP7 Network of Excellence PASCAL2 (ICT-216886-PASCAL2).

Selected & recent publications

• Juho Rousu, Daniel D. Agranoff, Olugbemiro Sodeinde, John Shawe-Taylor, Delmiro Fernandez-Reyes. Biomarker Discovery by Sparse Canonical Correlation Analysis of Complex Clinical Phenotypes of Tuberculosis and Malaria. PLOS Computational Biology 10.1371/journal.pcbi.1003018, 2013
• Markus Heinonen. Computational Methods for Small Molecules. PhD thesis. Report A-2012-9, Department of Computer Science, University of Helsinki
• Markus Heinonen, Huibin Shen, Nicola Zamboni, Juho Rousu. Metabolite identification and molecular fingerprint prediction through machine learning. Bioinformatics 28, 18 (2012), 2333-2341 
• Markus Heinonen, Niko Välimäki, Veli Mäkinen and Juho Rousu. Efficient Path Kernels for Reaction Function Prediction. Bioinformatics Models, Methods and Algorithms, 2012.
• Juho Rousu, Daniel D Agranoff, John Shawe-Taylor, Delmiro Fernandez-Reyes. Sparse Canonical Correlation Analysis for Biomarker Discovery: A Case Study in Tuberculosis (Extended abstract). Machine Learning in Systems Biology, 2011, pp 73-79
• Hongyu Su, Juho Rousu: Multi-Task Drug Bioactivity Classification with Graph Labeling Ensembles. Pattern Recognition in Bioinformatics, 2011, to appear
• Esa Pitkänen, Mikko Arvas, Juho Rousu: Minimum mutation algorithm for gapless metabolic network evolution. Bioinformatics Models, Methods and Algorithms, 2011.
• Hongyu Su, Markus Heinonen, Juho Rousu.Multilabel Classification of Drug-like Molecules via Max-Margin Conditional Random Fields. In Probabilistic Graphical Models, 2010. [dataset]
• Katja Astikainen, Liisa Holm, Esa Pitkanen, Sandor Szedmak and Juho Rousu. Structured Output Prediction of Novel Enzyme Function with Reaction Kernels. Springer Communications in Computer and Information Science,2011, volume 127, Part 5, Pages 367-379
• Markus Heinonen, Sampsa Lappalainen, Taneli Mielikäinen, Juho Rousu. Computing Atom Mappings for Biochemical Reactions without Subgraph Isomorphism. Journal of Computational Biology, 2011, 18(1): 43-58
• Esa Pitkänen, Juho Rousu, Esko Ukkonen: Computational methods for metabolic reconstruction. Current Opinion in Biotechnology, 21(1):70-77, 2010.
• Esa Pitkänen, Paula Jouhten, Juho Rousu: Inferring branching pathways in genome-scale metabolic networks. BMC Systems Biology, 3:103, 2009.
• 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.
• 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