Reference : Network deregulation analysis in complex diseases via the pairwise elastic net
Scientific congresses, symposiums and conference proceedings : Paper published in a book
Life sciences : Biochemistry, biophysics & molecular biology
http://hdl.handle.net/10993/11100
Network deregulation analysis in complex diseases via the pairwise elastic net
English
Vlassis, Nikos mailto [University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB) > >]
Glaab, Enrico mailto [University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB) > >]
2013
Proc 8th BeNeLux Bioinformatics Conference
Yes
8th BeNeLux Bioinformatics Conference
2013
[en] Complex diseases like neurodegenerative or cancer disorders are characterized by deregulations in multiple genes and proteins. Previous research has shown that neighboring genes in a molecular network tend to undergo coordinated expression changes. We describe an approach that allows identifying such jointly differentially expressed genes from input expression data and a graph encoding pairwise functional associations between genes (such as protein interactions). We cast this as a feature selection problem in penalized two-class (cases vs. controls) classification, and we propose a novel Pairwise Elastic Net penalty that favors the selection of discriminative genes according to their connectedness in the interaction graph. Experiments on microarray gene expression data for Parkinson’s disease demonstrate marked improvements in feature grouping over competitive methods.
Luxembourg Centre for Systems Biomedicine (LCSB): Machine Learning (Vlassis Group) ; Luxembourg Centre for Systems Biomedicine (LCSB): Biomedical Data Science (Glaab Group) ; Luxembourg Centre for Systems Biomedicine (LCSB): Bioinformatics Core (R. Schneider Group)
http://hdl.handle.net/10993/11100

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