@article {43410,
	title = {Building and analysis of protein-protein interactions related to diabetes mellitus using support vector machine, biomedical text mining and network analysis},
	journal = {Computational Biology and Chemistry},
	volume = {65},
	year = {2016},
	month = {DEC},
	pages = {37-44},
	abstract = {In order to understand the molecular mechanism underlying any disease, knowledge about the interacting proteins in the disease pathway is essential. The number of revealed protein-protein interactions (PPI) is still very limited compared to the available protein sequences of different organisms. Experiment based high-throughput technologies though provide some data about these interactions, those are often fairly noisy. Computational techniques for predicting protein protein interactions therefore assume significance. 1296 binary fingerprints that encode a combination of structural and geometric properties were developed using the crystallographic data of 15,000 protein complexes in the pdb server. In a case study, these fingerprints were created for proteins implicated in the Type 2 diabetes mellitus disease. The fingerprints were input into a SVM based model for discriminating disease proteins from non disease proteins yielding a classification accuracy of 78.2\% (AUC value of 0.78) on an external data set composed of proteins retrieved via text mining of diabetes related literature. A PPI network was constructed and analysed to explore new disease targets. The integrated approach exemplified here has a potential for identifying disease related proteins, functional annotation and other proteomics studies. (C) 2016 Elsevier Ltd. All rights reserved.},
	doi = {10.1016/j.compbiolchem.2016.09.011},
	author = {Vyas, Renu and Bapat, Sanket and Jain, Esha and Karthikeyan, Muthukumarasamy and Tambe, Sanjeev and Kulkarni, Bhaskar D.}
}
