GenePEN: analysis of network activity alterations in complex diseases via the pairwise elastic net

Vlassis Nikos and Glaab Enrico ()
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Vlassis Nikos: Adobe Research, Systems Technology Lab/Imagination Lab, 345 Park Avenue, San Jose, CA 95110, USA
Glaab Enrico: University of Luxembourg, Luxembourg Centre for Systems Biomedicine, 7, avenue des Hauts Fourneaux, Esch-sue-Alzette 4362, Luxembourg

Statistical Applications in Genetics and Molecular Biology, 2015, vol. 14, issue 2, 221-224

Abstract: Complex diseases are often characterized by coordinated expression alterations of genes and proteins which are grouped together in a molecular network. Identifying such interconnected and jointly altered gene/protein groups from functional omics data and a given molecular interaction network is a key challenge in bioinformatics. We describe GenePEN, a penalized logistic regression approach for sample classification via convex optimization, using a newly designed Pairwise Elastic Net penalty that favors the selection of discriminative genes/proteins according to their connectedness in a molecular interaction graph. An efficient implementation of the method finds provably optimal solutions on high-dimensional omics data in a few seconds and is freely available at http://lcsb-portal.uni.lu/bioinformatics.

Keywords: machine learning; microarray analysis; network analysis (search for similar items in EconPapers)
Date: 2015
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DOI: 10.1515/sagmb-2014-0045

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