Protein complex prediction based on k-connected subgraphs in protein interaction network.
BACKGROUND
Protein complexes play an important role in cellular mechanisms. Recently, several methods have been presented to predict protein complexes in a protein interaction network. In these methods, a protein complex is predicted as a dense subgraph of protein interactions. However, interactions data are incomplete and a protein complex does not have to be a complete or dense subgraph.


RESULTS
We propose a more appropriate protein complex prediction method, CFA, that is based on connectivity number on subgraphs. We evaluate CFA using several protein interaction networks on reference protein complexes in two benchmark data sets (MIPS and Aloy), containing 1142 and 61 known complexes respectively. We compare CFA to some existing protein complex prediction methods (CMC, MCL, PCP and RNSC) in terms of recall and precision. We show that CFA predicts more complexes correctly at a competitive level of precision.


CONCLUSIONS
Many real complexes with different connectivity level in protein interaction network can be predicted based on connectivity number. Our CFA program and results are freely available from http://www.bioinf.cs.ipm.ir/softwares/cfa/CFA.rar.
Habibi
Mahnaz
M
Faculty of Mathematics, Shahid-Beheshti University, gc, Tehran, Iran.
Eslahchi
Changiz
C
Wong
Limsoon
L
D000465:Algorithms; D016000:Cluster Analysis; D019295:Computational Biology / Q000379:methods*; D011485:Protein Binding; D021381:Protein Transport; D011506:Proteins / Q000378:metabolism*; D015203:Reproducibility of Results
D011506:Proteins