A large scale analysis of gene expression data, performed by Segal and colleagues, identified sets of genes named Cancer Mod- ules (CMs), involved in the onset and progression of cancer. By using functional interaction network data derived from different sources of biomolecular information, we show that random walks and label propaga- tion algorithms are able to correctly rank genes with respect to CMs. In particular, the random walk with restart algorithm (RWR), by exploit- ing both the global topology of the functional interaction network, and local functional connections between genes relatively close to CM genes, achieves significantly better results than the other compared methods, suggesting that RWR could be applied to discover novel genes involved in the biological processes underlying tumoral diseases.

Random walking on functional interaction networks to rank genes involved in cancer / M. Re, G. Valentini - In: Artificial intelligence applications and innovations : AIAI 2012 international Workshops: AIAB, AIeIA, CISE, COPA, IIVC, ISQL, MHDW, and WADTMB, Halkidiki, Greece, September 27-30, 2012 : proceedings, part II / [a cura di] L. Iliadis, I. Maglogiannis, H. Papadopoulos, K. Karatzas, S. Sioutas. - Heidelberg : Springer, 2012. - ISBN 9783642334115. - pp. 66-75 (( Intervento presentato al 2. convegno Artificial Intelligence Applications in Biomedicine Workshop tenutosi a Halkidiki, Greece nel 2012 [10.1007/978-3-642-33412-2_7].

Random walking on functional interaction networks to rank genes involved in cancer

M. Re
Primo
;
G. Valentini
Ultimo
2012

Abstract

A large scale analysis of gene expression data, performed by Segal and colleagues, identified sets of genes named Cancer Mod- ules (CMs), involved in the onset and progression of cancer. By using functional interaction network data derived from different sources of biomolecular information, we show that random walks and label propaga- tion algorithms are able to correctly rank genes with respect to CMs. In particular, the random walk with restart algorithm (RWR), by exploit- ing both the global topology of the functional interaction network, and local functional connections between genes relatively close to CM genes, achieves significantly better results than the other compared methods, suggesting that RWR could be applied to discover novel genes involved in the biological processes underlying tumoral diseases.
Settore INF/01 - Informatica
   Pattern Analysis, Statistical Modelling and Computational Learning 2
   PASCAL2
   EUROPEAN COMMISSION
   FP7
   216886
2012
IFIP
http://www.springerlink.com/content/6m6380u014252107/
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2434/206127
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