Title
Critical protein detection in dynamic PPI networks with multi-source integrated deep belief nets
Abstract
Critical node detection in dynamic networks is of great value in many areas, such as the evolving of friendship in social networks, the development of epidemics, molecular pathogenesis of diseases and so on. As for detecting critical nodes in dynamic Protein-Protein Interaction Networks (PPINs), there are mainly two challenges: the first is to construct the dynamic PPINs that are not available directly from biological experiments in laboratories; and the second is how to identify the most critical units that are responsible for the dynamic processes. This paper proposes effective framework to tackle these two problems. First of all, this paper proposes to construct the dynamic PPINs by simultaneously modeling the activity of proteins and assembling the dynamic co-regulation protein network at each time point. As result, more comprehensive dynamic PPINs are built. Besides, a novel critical protein detection method that integrates multiple PPI networks into a Deep Belief Network model (referred to as MIDBN) is developed. The integrated model is trained to get hierarchical common representations of multiple sources which are used to reconstruct the original data. The variabilities of the reconstruction errors across the time courses are ranked to finally get the top proteins that have significantly different evolving structural patterns than the other nodes in the dynamic networks. We evaluated our network construction method by comparing the functional representations of the derived networks with that of two other traditional construction methods, and our method achieved superior function analysis results. The ranking results of critical proteins from MIDBN were compared with results from two baseline methods and the comparison results showed that MIDBN had better reconstruction rate and identified more proteins of critical value to yeast cell cycle process.
Year
DOI
Venue
2013
10.1109/BIBM.2013.6732606
BIBM
Keywords
Field
DocType
critical value,functional representations,reconstruction error variabilities,hierarchical common representation,belief networks,dynamic protein-protein interaction networks,critical protein detection method,cellular biophysics,functional analysis,diseases,dynamic co-regulation protein network,social network,microorganisms,yeast cell cycle process,multisource integrated deep belief nets,network construction method,proteins,reconstruction rate,top proteins,comprehensive dynamic ppin,evolving structural pattern,molecular pathogenesis,multiple ppi network,superior function analysis,midbn,traditional construction method,dynamic network,effective framework,time courses,epidemics,epidemics development,proteomics,critical unit,dynamic ppi networks,deep belief network model,protein activity modeling,time point,original data reconstruction,critical node detection,medical computing
Deep belief nets,Data mining,Social network,Time point,Computer science,Deep belief network,Critical value,Cell cycle process,Artificial intelligence,Ranking,Bioinformatics,Multi-source,Machine learning
Conference
Volume
Issue
ISSN
null
null
2156-1125
Citations 
PageRank 
References 
0
0.34
10
Authors
6
Name
Order
Citations
PageRank
Yuan Zhang1446.45
Nan Du250352.49
Kang Li333729.74
Jinchao Feng4124.98
Ke-Bin Jia512644.30
Aidong Zhang62970405.63