Detecting recurrent gene mutation in interaction network context using multi-scale graph diffusion

Babaei, S.; Hulsman, M.; Reinders, M.J.T.; de Ridder, J.

Detecting recurrent gene mutation in interaction network context using multi-scale graph diffusion

Poster (2012)

Authors

S. Babaei Pattern Recognition and Bioinformatics - , Netherlands Bioinformatics Centre, Nijmegen

M. Hulsman Pattern Recognition and Bioinformatics -

M.J.T. Reinders Netherlands Bioinformatics Centre, Nijmegen, Pattern Recognition and Bioinformatics -

J. de Ridder Pattern Recognition and Bioinformatics - , Netherlands Bioinformatics Centre, Nijmegen

Research Group

Pattern Recognition and Bioinformatics () (TU Delft)

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http://resolver.tudelft.nl/uuid:63d26c61-25e6-4d3b-ba4b-66322077ee75

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Published Date

2012

Language

English

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Faculty

Electrical Engineering, Mathematics and Computer Science

Department

Intelligent Systems

Research Group

Pattern Recognition and Bioinformatics

Abstract

We introduce a multi-scale kernel diffusion framework and apply it to a large collection of murine retroviral insertional mutagenesis data. The diffusion strength plays the role of scale parameter. As a result, in addition to detecting genes with frequent mutations in their genomic vicinity (red nodes in the interaction graph) we can also find genes that harbor frequent mutations in their interaction network context (white and pink nodes).

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