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

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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)

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.

Suggested Citation

  • Vlassis Nikos & Glaab Enrico, 2015. "GenePEN: analysis of network activity alterations in complex diseases via the pairwise elastic net," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 14(2), pages 221-224, April.
    • Handle: RePEc:bpj:sagmbi:v:14:y:2015:i:2:p:221-224:n:4
    DOI: 10.1515/sagmb-2014-0045
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    References listed on IDEAS

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    1. Enrico Glaab & Jaume Bacardit & Jonathan M Garibaldi & Natalio Krasnogor, 2012. "Using Rule-Based Machine Learning for Candidate Disease Gene Prioritization and Sample Classification of Cancer Gene Expression Data," PLOS ONE, Public Library of Science, vol. 7(7), pages 1-18, July.
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