Discovering cooperating oncogenes
by statistical analysis of Retroviral Insertional Mutagenesis Data
J de Ridder (Nederlands Kanker Instituut - Antoni van Leeuwenhoek ziekenhuis, TU Delft - Pattern Recognition and Bioinformatics)
Lodewyk F. Wessels (TU Delft - Pattern Recognition and Bioinformatics, 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 (TU Delft - Pattern Recognition and Bioinformatics)
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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.
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