A NMF based approach for integrating multiple data sources to predict HIV-1–human PPIs

BMC Bioinformatics

Methodology Article

Sumanta Ray¹  Sanghamitra Bandyopadhyay² 

[1] Department of Computer Science and Engineering, Aliah University, Kolkata-700156, West Bengal, India;Machine Intelligence Unit, Indian Statistical Institute, Kolkata-700108, West Bengal, India;

关键词: Semantic Similarity;    Betweenness Centrality;    Coexpression Network;    Association Rule Mining;    Weighted Gene Coexpression Network Analysis;   

DOI  :  10.1186/s12859-016-0952-6

 received in 2015-06-30, accepted in 2016-02-05,  发布年份 2016

来源: Springer

【 摘 要 】

BackgroundPredicting novel interactions between HIV-1 and human proteins contributes most promising area in HIV research. Prediction is generally guided by some classification and inference based methods using single biological source of information.ResultsIn this article we have proposed a novel framework to predict protein-protein interactions (PPIs) between HIV-1 and human proteins by integrating multiple biological sources of information through non negative matrix factorization (NMF). For this purpose, the multiple data sets are converted to biological networks, which are then utilized to predict modules. These modules are subsequently combined into meta-modules by using NMF based clustering method. The integrated meta-modules are used to predict novel interactions between HIV-1 and human proteins. We have analyzed the significant GO terms and KEGG pathways in which the human proteins of the meta-modules participate. Moreover, the topological properties of human proteins involved in the meta modules are investigated. We have also performed statistical significance test to evaluate the predictions.ConclusionsHere, we propose a novel approach based on integration of different biological data sources, for predicting PPIs between HIV-1 and human proteins. Here, the integration is achieved through non negative matrix factorization (NMF) technique. Most of the predicted interactions are found to be well supported by the existing literature in PUBMED. Moreover, human proteins in the predicted set emerge as ‘hubs’ and ‘bottlenecks’ in the analysis. Low p-value in the significance test also suggests that the predictions are statistically significant.

【 授权许可】

CC BY   
© Ray and Bandyopadhyay. 2016

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