KAUST DepartmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Computer Science Program
Permanent link to this recordhttp://hdl.handle.net/10754/564351
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AbstractFunctional module discovery aims to find well-connected subnetworks which can serve as candidate protein complexes. Advances in High-throughput proteomic technologies have enabled the collection of large amount of interaction data as well as gene expression data. We propose, CLARM, a clustering algorithm that integrates gene expression profiles and protein protein interaction network for biological modules discovery. The main premise is that by enriching the interaction network by adding interactions between genes which are highly co-expressed over a wide range of biological and environmental conditions, we can improve the quality of the discovered modules. Protein protein interactions, known protein complexes, and gene expression profiles for diverse environmental conditions from the yeast Saccharomyces cerevisiae were used for evaluate the biological significance of the reported modules. Our experiments show that the CLARM approach is competitive to wellestablished module discovery methods. Copyright © 2011 ACM.
JournalProceedings of the 2nd ACM Conference on Bioinformatics, Computational Biology and Biomedicine - BCB '11
Conference/Event name2011 ACM Conference on Bioinformatics, Computational Biology and Biomedicine, ACM-BCB 2011