Discovering cancer pathways
by inferring combinatorial association logic
J de Ridder (Nederlands Kanker Instituut - Antoni van Leeuwenhoek ziekenhuis, TU Delft - Pattern Recognition and Bioinformatics)
J.J. Bot (TU Delft - Pattern Recognition and Bioinformatics)
Jaap Kool (Nederlands Kanker Instituut - Antoni van Leeuwenhoek ziekenhuis)
Anthony Uren (Nederlands Kanker Instituut - Antoni van Leeuwenhoek ziekenhuis)
Lodewyk F.A. Wessels (TU Delft - Pattern Recognition and Bioinformatics, Nederlands Kanker Instituut - Antoni van Leeuwenhoek ziekenhuis)
Marcel J. T. Reinders (TU Delft - Pattern Recognition and Bioinformatics)
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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).
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