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Network deregulation analysis in complex diseases via the pairwise elastic net
Vlassis, Nikos; Glaab, Enrico
2013In Proc 8th BeNeLux Bioinformatics Conference
Peer reviewed
 

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Abstract :
[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.
Research center :
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)
Disciplines :
Biochemistry, biophysics & molecular biology
Author, co-author :
Vlassis, Nikos ;  University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB)
Glaab, Enrico  ;  University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB)
External co-authors :
no
Language :
English
Title :
Network deregulation analysis in complex diseases via the pairwise elastic net
Publication date :
2013
Event name :
8th BeNeLux Bioinformatics Conference
Event date :
2013
Main work title :
Proc 8th BeNeLux Bioinformatics Conference
Peer reviewed :
Peer reviewed
Available on ORBilu :
since 17 November 2013

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