Discovering cancer pathways

by inferring combinatorial association logic

Poster (2009)

Authors

J. de Ridder Nederlands Kanker Instituut - Antoni van Leeuwenhoek ziekenhuis, Pattern Recognition and Bioinformatics - EEMCS
J.J. Bot Pattern Recognition and Bioinformatics - EEMCS
Jaap Kool Nederlands Kanker Instituut - Antoni van Leeuwenhoek ziekenhuis
Anthony Uren Nederlands Kanker Instituut - Antoni van Leeuwenhoek ziekenhuis
L.F.A. Wessels Pattern Recognition and Bioinformatics - EEMCS , Nederlands Kanker Instituut - Antoni van Leeuwenhoek ziekenhuis
M.J.T. Reinders Pattern Recognition and Bioinformatics - EEMCS
Research Group
Pattern Recognition and Bioinformatics (EEMCS) (TU Delft)
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Published Date

2009

Language

English

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Faculty

Electrical Engineering, Mathematics and Computer Science

Department

Intelligent Systems

Research Group

Pattern Recognition and Bioinformatics

Abstract

In this study, 43 tumors that were induced by retroviral insertional mutagenesis are expression profiled, resulting in a dataset for which both the initiating events (the viral integration sites) as well as the consequent expression profiles are available.
To capture complex associations that arise due to interaction among insertion target genes, we infer small Boolean logic networks that explicitly incorporate operators to model the potential parallel alternatives (‘exclusive-or’ gates) as well as the potential cooperation between mutations (‘and’ gates).