The Peroxisome proliferators-activated receptors (PPARs) constitute a highly conserved set of ligand activated transcription factors in the nuclear hormone receptor subfamily. Selective modulation of PPAR could provide significant anti-diabetic activity with the reduction or elimination of side effects. These have increasingly become attractive targets for developing novel anti-type 2 diabetic ...

A QSAR study on thiophenyl derivatives as SGLT2 inhibitors as potential antidiabetic agents was performed with thirty-three compounds. Comparison of the obtained results indicated the superiority of the genetic algorithm over the simulated annealing and stepwise forward-backward variable method for feature selection. The best 2D QSAR model showed satisfactory statistical parameters for the data...

Epidermal growth factor receptor (EGFR) protein tyrosine kinases (PTKs) are known for its role in cancer. Quinazoline have been reported to be the molecules of interest, with potent anticancer activity and they act by binding to ATP site of protein kinases. ATP binding site of protein kinases provides an extensive opportunity to design newer analogs. With this background, we report an attempt t...

Background and aim: Maternal concept is part of the feminine gender role. The important part of the maternal concept is the unique relationship experience between mother and child that begins with  maternal-fetal attachment(MFA) during pregnancy. The aim of this study is predict the MFA according to Gender role in pregnant women in Shiraz city. Methods:This descriptive correlational study was c...

In data mining applications, one of the useful algorithms for classification is the kNN algorithm. The kNN search has a wide usage in many research and industrial domains like 3-dimensional object rendering, content-based image retrieval, statistics, biology (gene classification), etc. In spite of some improvements in the last decades, the computation time required by the kNN search remains the...

KNN algorithm is a simple, effective, non-parametric classification, and has been widely used in text classification, pattern recognition, image and spatial classification. Research on improvements about KNN algorithm has broad application prospects and important scientific significance. Based on analysis about classic KNN and its improved algorithms, we find its over-reliance on the choice of ...

Noise-corrupted signals and images can be reconstructed by minimization of a Hamiltonian. Often this Hamiltonian is a non-convex functional. The solution of minimum energy can then be approximated by the Graduated Non-Convexity (GNC) algorithm developed for the weak membrane by Blake and Zisserman. The GNC approximates the non-convex functional by a convex functional, and varies the solution sp...

The k Nearest Neighbor (kNN) join operation associates each data object in one data set with its k nearest neighbors from the same or a different data set. The kNN join on high-dimensional data (high-dimensional kNN join) is an especially expensive operation. Existing high-dimensional kNN join algorithms were designed for static data sets and therefore cannot handle updates efficiently. In this...

KNN is one of the most popular classification methods, but it often fails to work well with inappropriate choice of distance metric or due to the presence of numerous class-irrelevant features. Linear feature transformation methods have been widely applied to extract class-relevant information to improve kNN classification, which is very limited in many applications. Kernels have been used to l...