José Caldas,

Helsinki University of Technology


As ArrayExpress and other repositories of genome-
wide experiments are reaching a mature size, it is becoming more
meaningful to search for related experiments, given a particular
study. We introduce methods that allow for the search to be
based upon measurement data, instead of the more customary
annotation data. The goal is to retrieve experiments in which the
same biological processes are activated. This can be due either
to experiments targeting the same biological question, or to as-yet
unknown relationships. We use a combination of existing and new probabilistic
machine learning techniques to extract information about the
biological processes differentially activated in each experiment, to
retrieve earlier experiments where the same processes are activated,
and to visualize and interpret the retrieval results. Case studies on a
subset of ArrayExpress show that, with a sufficient amount of data,
our method indeed finds experiments relevant to particular biological
questions. Results can be interpreted in terms of biological processes
using the visualization techniques.


Date: 2009-Apr-15     Time: 16:00:00     Room: 336

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