Statistical mining of biological data
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
Website: http://www.cis.hut.fi/projects/mi/
Publications
Nikkilä, J. & Honkela, A. & Kaski, S: Exploring the independence of gene regulatory modules In Juho Rousu, Samuel Kaski, and Esko Ukkonen, editors, Proceedings of PMSB 2006, Probabilistic Modeling and Machine Learning in Structural and Systems Biology, pp. 131-136, 2006.
Klami, A. & Kaski, S. Generative models that discover dependencies between data sets. In Proceedings of IEEE Workshop on Machine Learning for Signal Processing (MLSP'06), pages 123-128. IEEE 2006.
Oja, M. & Peltonen, J. & Kaski, S: Estimation of human endogenous retrovirus activities from expressed sequence databases. In Juho Rousu, Samuel Kaski, and Esko Ukkonen, editors, Proceedings of PMSB 2006, Probabilistic Modeling and Machine Learning in Structural and Systems Biology, pages 50-54, 2006.
Venna, J. & Kaski. S: Local multidimensional scaling. Neural Networks, 19, pp 889--899, 2006.
Venna, J. & Kaski, S: Visualizing Gene Interaction Graphs with Local Multidimensional Scaling. In Proceedings of 14th European Symposium on Artificial Neural Networks (ESANN'06), pp. 557-562, 2006.