@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.}
}
@conference {45756,
	title = {Biomedical literature mining for protein-protein interactions analysis using electronic mailing system},
	booktitle = {253rd National Meeting of the American-Chemical-Society (ACS) on Advanced Materials, Technologies, Systems, and Processes},
	year = {2017},
	month = {APR },
	publisher = {Amer chemical soc, 1155 16TH ST, NW, Washington, DC 20036 USA},
	organization = {Amer chemical soc, 1155 16TH ST, NW, Washington, DC 20036 USA},
	address = { San Francisco, CA},
	author = {Karthikeyan, Muthukumarasamy and Vyas, Renu}
}
@article {48370,
	title = {BESFA: bioinformatics based evolutionary, structural \& functional analysis of prostate, Placenta, Ovary, Testis, and Embryo (POTE) paralogs},
	journal = {Heliyon},
	volume = {8},
	year = {2022},
	month = {SEP},
	pages = {e10476},
	type = {Article},
	abstract = {<p>
	The POTE family comprises 14 paralogues and is primarily expressed in Prostate, Placenta, Ovary, Testis, Embryo (POTE), and cancerous cells. The prospective function of the POTE protein family under physiological conditions is less understood. We systematically analyzed their cellular localization and molecular docking analysis to elucidate POTE proteins{\textquoteright} structure, function, and Adaptive Divergence. Our results suggest that group three POTE paralogs (POTEE, POTEF, POTEI, POTEJ, and POTEKP (a pseudogene)) exhibits significant variation among other members could be because of their Adaptive Divergence. Furthermore, our molecular docking studies on POTE protein revealed the highest binding affinity with NCI-approved anticancer compounds. Additionally, POTEE, POTEF, POTEI, and POTEJ were subject to an explicit molecular dynamic simulation for 50ns. MM-GBSA and other essential electrostatics were calculated that showcased that only POTEE and POTEF have absolute binding affinities with minimum energy exploitation. Thus, this study{\textquoteright}s outcomes are expected to drive cancer research to successful utilization of POTE genes family as a new biomarker, which could pave the way for the discovery of new therapies.</p>
},
	keywords = {(MMGBSA), Adaptive divergence, Evolution, generalized born surface area, Homology, Mechanics, Molecular docking, Molecular dynamic simulation molecular, POTE paralogs},
	doi = {10.1016/j.heliyon.2022.e10476},
	author = {Qazi, Sahar and Jit, Bimal Prasad and Das, Abhishek and Karthikeyan, Muthukumarasamy and Saxena, Amit and Ray, M. D. and Singh, Angel Rajan and Raza, Khalid and Jayaram, B. and Sharma, Ashok}
}
