title: GenePEN: analysis of network activity alterations in complex diseases via the pairwise elastic net.
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.
authors.forename: Nikos
authors.initials: N
authors.lastname: Vlassis
authors.forename: Enrico
authors.initials: E
authors.lastname: Glaab
mesh_terms: D000465:Algorithms; D019295:Computational Biology / Q000379:methods*; D003198:Computer Simulation; D053263:Gene Regulatory Networks / Q000235:genetics*; D005796:Genes / Q000235:genetics; D016015:Logistic Models; D011506:Proteins / Q000235:genetics; D012984:Software
chemical_list: D011506:Proteins