Paper Info
Title
Predicting Protein Functions Based on Dynamic Protein Interaction Networks.
Abstract
Accurate annotation of protein functions plays a significant role in understanding life at the molecular level. With accumulation of sequenced genomes, the gap between available sequence data and their functional annotations has been widening. Many computational methods have been proposed to predict protein function from protein-protein interaction (PPI) networks. However, the precision of function prediction still needs to be improved. Taking into account the dynamic nature of PPIs, we construct a dynamic protein interactome network by integrating PPI network and gene expression data. To reduce the negative effect of false positive and false negative on the protein function prediction, we predict and generate some new protein interactions combing with proteins' domain information and protein complex information and weight all interactions. Based on the weighted dynamic network, we propose a method for predicting protein functions, named PDN. After traversing all the different dynamic networks, a set of candidate neighbors is formed. Then functions derived from the set of candidates are scored and sorted, according to the weighted degree of candidate proteins. Experimental results on four different yeast PPI networks indicate that the accuracy of PDN is 18% higher than other competing methods.
Year
DOI
Venue
2015
10.1007/978-3-319-19048-8_33
BIOINFORMATICS RESEARCH AND APPLICATIONS (ISBRA 2015)
Keywords
Field
DocType
Protein-protein interaction,Functions prediction,Dynamic networks,PDN
Genome,Dynamic network analysis,Protein Interaction Networks,Protein–protein interaction,Interactome,Annotation,Computer science,Artificial intelligence,Protein function,Bioinformatics,Protein function prediction,Machine learning
Conference
Volume
ISSN
Citations 
9096
0302-9743
0
PageRank 
References 
Authors
0.34
10
4
Name
Order
Citations
PageRank
Bihai Zhao1243.74
Jianxin Wang22163283.94
FangXiang Wu376076.89
Yi Pan42507203.23