Bioinformatics Advance Access originally published online on April 21, 2006
Bioinformatics 2006 22(13):1623-1630; doi:10.1093/bioinformatics/btl145
| ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Exploiting indirect neighbours and topological weight to predict protein function from protein–protein interactions
1 Graduate School for Integrated Sciences and Engineering, National University of Singapore Singapore
2 School of Computing, National University of Singapore Singapore
*To whom correspondence should be addressed.
Motivation: Most approaches in predicting protein function from protein–protein interaction data utilize the observation that a protein often share functions with proteins that interacts with it (its level-1 neighbours). However, proteins that interact with the same proteins (i.e. level-2 neighbours) may also have a greater likelihood of sharing similar physical or biochemical characteristics. We speculate that functional similarity between a protein and its neighbours from the two different levels arise from two distinct forms of functional association, and a protein is likely to share functions with its level-1 and/or level-2 neighbours. We are interested in finding out how significant is functional association between level-2 neighbours and how they can be exploited for protein function prediction.
Results: We made a statistical study on recent interaction data and observed that functional association between level-2 neighbours is clearly observable. A substantial number of proteins are observed to share functions with level-2 neighbours but not with level-1 neighbours. We develop an algorithm that predicts the functions of a protein in two steps: (1) assign a weight to each of its level-1 and level-2 neighbours by estimating its functional similarity with the protein using the local topology of the interaction network as well as the reliability of experimental sources and (2) scoring each function based on its weighted frequency in these neighbours. Using leave-one-out cross validation, we compare the performance of our method against that of several other existing approaches and show that our method performs relatively well.
Contact: g0306417{at}nus.edu.sg
Received on October 15, 2005; revised on February 14, 2006; accepted on April 11, 2006
CiteULike 
Connotea 
Del.icio.us What's this?
This article has been cited by other articles:
|
|
 |
|
  G. Liu, L. Wong, and H. N. Chua Complex discovery from weighted PPI networks Bioinformatics, August 1, 2009; 25(15): 1891 - 1897. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 B. Andreopoulos, A. An, X. Wang, and M. Schroeder A roadmap of clustering algorithms: finding a match for a biomedical application Brief Bioinform, May 1, 2009; 10(3): 297 - 314. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 Y. Qi, Y. Suhail, Y.-y. Lin, J. D. Boeke, and J. S. Bader Finding friends and enemies in an enemies-only network: A graph diffusion kernel for predicting novel genetic interactions and co-complex membership from yeast genetic interactions Genome Res., December 1, 2008; 18(12): 1991 - 2004. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
Y. Li, P. Agarwal, and D. Rajagopalan A global pathway crosstalk network Bioinformatics, June 15, 2008; 24(12): 1442 - 1447. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
H. N. Chua, W.-K. Sung, and L. Wong An efficient strategy for extensive integration of diverse biological data for protein function prediction Bioinformatics, December 15, 2007; 23(24): 3364 - 3373. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
S. Asur, D. Ucar, and S. Parthasarathy An ensemble framework for clustering protein protein interaction networks Bioinformatics, July 1, 2007; 23(13): i29 - i40. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
B. Andreopoulos, A. An, X. Wang, M. Faloutsos, and M. Schroeder Clustering by common friends finds locally significant proteins mediating modules Bioinformatics, May 1, 2007; 23(9): 1124 - 1131. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
A. Li and S. Horvath Network neighborhood analysis with the multi-node topological overlap measure Bioinformatics, January 15, 2007; 23(2): 222 - 231. [Abstract] [Full Text] [PDF]
|
|