Mepco Schlenk Engineering College

About: Mepco Schlenk Engineering College is a based out in . It is known for research contribution in the topics: Wavelet & Wavelet transform. The organization has 1307 authors who have published 1665 publications receiving 18690 citations.

In silico molecular docking and physicochemical property studies on effective phytochemicals targeting GPR116 for breast cancer treatment.

Abstract: G protein-coupled receptor 116 (GPR116), an orphan adhesion receptor, found an important role in cell adhesion and migration in eukaryotes. Abnormal expression of GPCR identified in various cancers turns focus of research community towards GPCR to identify the targeting drug against GPCR. Though GPR116 role was studied in progression of metastasis in triple-negative breast cancer (TNBC), unfortunately, still no drugs targeting GPR116 were identified. TNBC is a hormone-negative aggressive breast cancer found even in young women. Since TNBC has no target receptor for therapy, it would be desirable to target GPR116. Currently, chemotherapy is the only promising option for TNBC; however, these drugs cause chemoresistance. Hence this current study concentrated on finding drugable natural phytochemical ligands targeting GPR116 using in silico approach. Best docked ligand with target and active binding site amino acids were identified in molecular docking study. Pharmacokinetic properties (ADME) were assessed by Qikprop. Result showed that pharmacokinetics properties of natural phytochemicals were as good as existing chemotherapeutic cancer drugs. This study indicates that phytochemicals could be a promising target for GPR116. This in silico analysis facilitates further research to design the drug targeting GPR116 for treatment of TNBC.

Rule reduction in fuzzy logic for better interpretability in reservoir operation

Abstract: Decision-making in reservoir operation has become easy and understandable with the use of fuzzy logic models, which represent the knowledge in terms of interpretable linguistic rules. However, the improvement in interpretability with increase in number of fuzzy sets (‘low’, ‘high’, etc) comes with the disadvantage of increase in number of rules that are difficult to comprehend by decision makers. In this study, a clustering-based novel approach is suggested to provide the operators with a limited number of most meaningful operating rules. A single triangular fuzzy set is adopted for different variables in each cluster, which are fine-tuned with genetic algorithm (GA) to meet the desired objective. The results are compared with the multi fuzzy set fuzzy logic model through a case study in the Pilavakkal reservoir system in Tamilnadu State, India. The results obtained are highly encouraging with a smaller set of rules representing the actual fuzzy logic system. Copyright © 2007 John Wiley & Sons, Ltd.