Statistical mining of biological data
The project team develops machine-learning methods for data-mining, information-visualisation and statistical modelling. Here, machine-learning means flexible models that can be used in several application areas.
The methods are developed in the scope of bioinformatics and information-retrieval projects, where we cooperate with projects groups working in the application field. The applications act as test cases for the new methods, and the methods reciprocate by solving problems in the application field.
The research of the group currently focuses on discriminative generative modelling, data fusion by modelling dependencies between data sets, supervised unsupervised learning, and models for defining and extracting ‘relevant' signals from data.
The project work is divided between the Department of Computer Science and the Laboratory of Computer and Information Science at Helsinki University of Technology.
Contact person: Professor Samuel Kaski
Homepage: http://www.cis.hut.fi/projects/mi/
Publications
S. Kaski, J. Nikkilä, J. Sinkkonen, L. Lahti, J. Knuuttila, and C. Roos:
Associative clustering for exploring dependencies between functional genomics data sets.
IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2:203-216, 2005.
S. Kaski, J. Sinkkonen, and A. Klami: Discriminative clustering. Neurocomputing, 69:18-41, 2005.
J. Nikkilä, C. Roos, E. Savia, and S. Kaski: Explorative modeling of yeast stress response and its regulation with gCCA and associative clustering. International Journal of Neural Systems, 15:237-246, 2005.
M. Oja, G. O. Sperber, J. Blomberg, and S. Kaski: Self-organizing map- based discovery and visualization of human endogenous retroviral sequence groups. International Journal of Neural Systems, 15:163-180, 2005.
J. Peltonen and S. Kaski: Discriminative components of data. IEEE Transactions on Neural Networks, 16:68-83, 2005.