Reference : GenePEN: analysis of network activity alterations in complex diseases via the pairwis...
Scientific journals : Article
Life sciences : Biotechnology
http://hdl.handle.net/10993/19958
GenePEN: analysis of network activity alterations in complex diseases via the pairwise elastic net
English
Vlassis, Nikos mailto [Adobe Research > Systems Technology Lab/Imagination Lab]
Glaab, Enrico mailto [University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB) > >]
2015
Statistical Applications in Genetics and Molecular Biology
University of California, Berkeley
14
2
221-224
Yes (verified by ORBilu)
International
1544-6115
Berkeley
CA
[en] machine learning ; microarray analysis ; network analysis
[en] 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.
<br />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.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.
<br />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.
Luxembourg Centre for Systems Biomedicine (LCSB): Biomedical Data Science (Glaab Group) ; Luxembourg Centre for Systems Biomedicine (LCSB): Bioinformatics Core (R. Schneider Group) ; Luxembourg Centre for Systems Biomedicine (LCSB): Machine Learning (Vlassis Group)
Researchers ; Professionals ; Students
http://hdl.handle.net/10993/19958
also: http://hdl.handle.net/10993/20293
10.1515/sagmb-2014-0045.
http://www.degruyter.com/view/j/sagmb.2015.14.issue-2/sagmb-2014-0045/sagmb-2014-0045.xml

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