MULTIBIO

Computational data fusion of multiple biological information sources and background data (MULTIBIO) of the Statistical Machine Learning and Bioinformatics group is a project funded by TEKES, under the Algorithmic Data Analysis research programme.

A current problem in biological and medical research is how to use existing biological knowledge and heterogeneous experimental data in making inferences on new data. We study new computational methods and theory for the fusion of multiple biological information sources with partially-relevant background data from existing and new databanks. We argue that using the available public or private background information from hundreds of different situations or conditions, it is potentially possible to both complement the existing scarce data and to focus the analysis on relevant variables.

The project complements the task-dependent bioinformatics methods, which are naturally required in all biological and medical research problems as well, with methods that address a key underlying statistical limitation in current studies using high-throughput measurement techniques: large p, small n. It is very hard to make trustworthy computational models or statistically significant diagnoses based on only few samples (small n) when the number of studied genes or metabolites (p) is large.

For more information including publications see main project page.