Abstract
Algorithms for detection of modules in genetics interaction networks, while often identifying new models of functional modular organization between genes, have been limited to the output of disjoint, non-overlapping modules, while natural overlapping modules have been observed in biological networks. We present CLOVER, an algorithm for clustering weighted networks into overlapping clusters. We apply this algorithm to the correlation network obtained from a large-scale genetic interaction network of Saccharomyces cerevisiae derived from Synthetic Genetic Arrays (SGA) that covers ∼4,500 nonessential genes. We compare CLOVER to previous clustering methods, and demonstrate that genes assigned by our method to multiple clusters known to link distinct biological processes.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
