Building and analysis of protein-protein interactions related to diabetes mellitus using support vector machine, biomedical text mining and network analysis
| Title | Building and analysis of protein-protein interactions related to diabetes mellitus using support vector machine, biomedical text mining and network analysis |
| Publication Type | Journal Article |
| Year of Publication | 2016 |
| Authors | Vyas, R, Bapat, S, Jain, E, Karthikeyan, M, Tambe, S, Kulkarni, BD |
| Journal | Computational Biology and Chemistry |
| Volume | 65 |
| Pagination | 37-44 |
| Date Published | DEC |
| 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 |
| Type of Journal (Indian or Foreign) | Foreign |
| Impact Factor (IF) | 1.014 |
Divison category:
Chemical Engineering & Process Development