Abstract:
BACKGROUND:The current availability of public protein-protein interaction (PPI) databases which are usually modelled as PPI networks has led to the rapid development of protein function prediction approaches. The existing network-based prediction approaches mainly focus on the topological similarities between immediately interacting proteins, neglecting the protein functional connectivity which is the functional tightness between proteins. In this paper, we attempt to predict the functions of unannotated proteins based on PPI networks by incorporating the protein functional connectivity, as well as the similarity of protein functions, into the prediction procedure. RESULTS:An approach named Semantic protein function Prediction based on protein Functional Connectivity (SPFC) is proposed to achieve a higher accuracy in predicting functions of unannotated protein. We define the functional connectivity and function addition for each protein, and incorporate them into the prediction. We evaluated the SPFC on real PPI datasets and the experiment results show that the SPFC method is more effective in function prediction than other network-based approaches. CONCLUSION:Incorporating the functional connectivity of each protein into the function prediction can significantly improve the accuracy of protein prediction.
journal_name
Comput Biol Chemjournal_title
Computational biology and chemistryauthors
Zhu W,Hou J,Chen YPdoi
10.1016/j.compbiolchem.2013.01.002subject
Has Abstractpub_date
2013-06-01 00:00:00pages
9-14eissn
1476-9271issn
1476-928Xpii
S1476-9271(13)00004-2journal_volume
44pub_type
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