PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Probabilistic retrieval and visualization of biologically relevant microarray experiments
José Caldas, Nils Gehlenborg, Ali Faisal, Alvis Brazma and Samuel Kaski
Bioinformatics Volume 25, i145-i153, 2009.


Motivation: 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. Results: 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. Availability: The code is available from

EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Theory & Algorithms
Information Retrieval & Textual Information Access
ID Code:6288
Deposited By:Samuel Kaski
Deposited On:08 March 2010