Discovering cooperating oncogenes

by statistical analysis of Retroviral Insertional Mutagenesis Data

Poster (2006)

Authors

J. de Ridder Nederlands Kanker Instituut - Antoni van Leeuwenhoek ziekenhuis, Pattern Recognition and Bioinformatics - EEMCS
L.F.A. Wessels Pattern Recognition and Bioinformatics - EEMCS , Nederlands Kanker Instituut - Antoni van Leeuwenhoek ziekenhuis
Anthony Uren Nederlands Kanker Instituut - Antoni van Leeuwenhoek ziekenhuis
Jaap Kool 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

2006

Language

English

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Faculty

Electrical Engineering, Mathematics and Computer Science

Department

Intelligent Systems

Research Group

Pattern Recognition and Bioinformatics

Abstract

Viruses can induce oncogenic mutations when inserted near (or within) proto-oncogenes. Cancer genes can be identified by determining the loci of viral
insertions from tumors induced by retroviruses. Most often, multiple co-occurring mutations are needed for a cell to develop into a tumor. We propose a 2D Gaussian Kernel Convolution method to discover the cooperating oncogenes from publicly available retroviral insertional mutagenesis data.