Algorithms for detecting protein complexes in PPI networks: an evaluation study
datasetposted on 21.11.2017 by Wu, Min, Li, Xiaoli, Kwoh, Chee-Keong
Datasets usually provide raw data for analysis. This raw data often comes in spreadsheet form, but can be any collection of data, on which analysis can be performed.
Since protein complexes play important biological roles in cells, many computational methods have been proposed to detect protein complexes from protein-protein interaction (PPI) data. In this paper, we first review four reputed protein-complex detection algorithms (MCODE, MCL, CPA and DECAFF) and then present a comprehensive evaluation among them on two popular yeast PPI data3. We also discuss their relative strengthes and disadvantages to guide interested researchers. PRIB 2008 proceedings found at: http://dx.doi.org/10.1007/978-3-540-88436-1 Contributors: Monash University. Faculty of Information Technology. Gippsland School of Information Technology ; Chetty, Madhu ; Ahmad, Shandar ; Ngom, Alioune ; Teng, Shyh Wei ; Third IAPR International Conference on Pattern Recognition in Bioinformatics (PRIB) (3rd : 2008 : Melbourne, Australia) ; Coverage: Rights: Copyright by Third IAPR International Conference on Pattern Recognition in Bioinformatics. All rights reserved.