Multiple data sources in functional genomics -

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We address a fundamental data-analytic limitation of genome-wide microarray measurements. The number of genes that can be measured at a time is already huge but the number of samples (microarrays) is small and limited by the measurement cost and sample availability. Hence, the relative number of representative samples per gene is always very small, and the problem will persist; in new experimental settings there never exists representative data a priori. This makes accurate data analysis difficult and increases the chances of false discoveries when targeting a holistic view of the cell, based on the noisy high-dimensional data.

Our bioinformatics research problem is how to take advantage of existing, partially representative data sets of different types to support inferences in biological and medical questions. If this problem can be solved, data analysis methods could use the accumulating body of data, part of which may be publicly available, in supporting genome-wide inferences in new settings and research questions.

The developed methods will be applied in a representative set of research problems in two biomedical areas: cancer research and neuroscience.