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PLoS One. 2014 Jan 1;9(1):e84227. doi: 10.1371/journal.pone.0084227. eCollection 2014.

Automatic context-specific subnetwork discovery from large interaction networks.

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Department of Computer Science and Engineering, Korea University, Seoul, Korea.
Department of Medicine/Medical Oncology, University of Colorado Anschutz Medical Campus, Aurora, Colorado, United States of America.
Department of Computer Science and Engineering, Korea University, Seoul, Korea ; Interdisciplinary Graduate Program in Bioinformatics, Korea University, Seoul, Korea.


Genes act in concert via specific networks to drive various biological processes, including progression of diseases such as cancer. Under different phenotypes, different subsets of the gene members of a network participate in a biological process. Single gene analyses are less effective in identifying such core gene members (subnetworks) within a gene set/network, as compared to gene set/network-based analyses. Hence, it is useful to identify a discriminative classifier by focusing on the subnetworks that correspond to different phenotypes. Here we present a novel algorithm to automatically discover the important subnetworks of closely interacting molecules to differentiate between two phenotypes (context) using gene expression profiles. We name it COSSY (COntext-Specific Subnetwork discoverY). It is a non-greedy algorithm and thus unlikely to have local optima problems. COSSY works for any interaction network regardless of the network topology. One added benefit of COSSY is that it can also be used as a highly accurate classification platform which can produce a set of interpretable features.

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