In the present work, the support vector machine (SVM) and Adaboost-SVM have been used to develop a classification model as a potential screening mechanism for a novel series of 5-HT(1A) selective ligands. Each compound is represented by calculated structural descriptors that encode topological features. The particle swarm optimization (PSO) and the stepwise multiple linear regression (Stepwise-...

Quantitative relationships between molecular structure and azolo-adamantanes derivatives were discovered by different chemometric tools including factor analysis based multiple linear regressions (FA-MLR), principle component regression analysis (PCRA), and genetic algorithm-partial least squares GA-PLS. The FA-MLR describes the effect of geometrical and quantum indices on enzyme inhibition act...

The data is obtained from exploring the modulatory activities of bioflavonoids on P-glycoprotein function by ligand-based approaches. Multivariate Linear-QSAR models for predicting the induced/inhibitory activities of the flavonoids were created. Molecular descriptors were initially used as independent variables and a dependent variable was expressed as pFAR. The variables were then used in MLR...

Due to a boom in the dairy industry in Northeast China, the hay industry has been developing rapidly. Thus, it is very important to evaluate the hay quality with a rapid and accurate method. In this research, a novel technique that combines near infrared spectroscopy (NIRs) with three different statistical analyses (MLR, PCR and PLS) was used to predict the chemical quality of sheepgrass (Leymu...

The study reported here examined, as the research subject, surface soils in the Liuxin mining area of Xuzhou, and explored the heavy metal content and spectral data by establishing quantitative models with Multivariable Linear Regression (MLR), Generalized Regression Neural Network (GRNN) and Sequential Minimal Optimization for Support Vector Machine (SMO-SVM) methods. The study results are as ...

BACKGROUND In the nutrition literature, there are several reports on the use of artificial neural network (ANN) and multiple linear regression (MLR) approaches for predicting feed composition and nutritive value, while the use of support vector machines (SVM) method as a new alternative approach to MLR and ANN models is still not fully investigated. METHODS The MLR, ANN, and SVM models were d...

The asymmetric localization of mRNA plays an important role in coordinating posttranscriptional events in eukaryotic cells. We investigated the peripheral mitochondrial localization of nuclear-encoded mRNAs (MLR) in various conditions in which the mRNA binding protein context and the translation efficiency were altered. We identified Puf3p, a Pumilio family RNA-binding protein, as the first tra...

The objectives of this trial were to develop multiple linear regression (MLR) models and three-layer Levenberg-Marquardt back propagation (BP3) neural network models using the Cornell Net Carbohydrate and Protein System (CNCPS) carbohydrate fractions as dietary variables for predicting in vitro rumen volatile fatty acid (VFA) production and further compare MLR and BP3 models. Two datasets were ...

On our astronomical neighbors Mars and the Moon, bouncing movements are the preferred locomotor techniques. During bouncing, the stretch-shortening cycle describes the muscular activation pattern. This study aimed to identify gravity-dependent changes in kinematic and neuromuscular characteristics in the stretch-shortening cycle. Hence, neuromuscular control of limb muscles as well as correlati...

Neural stem cells (NSCs) transplantation has been proposed as a means of restoring damaged brain tissue, a possibility rendered more likely by reports of low NSCs immunogenicity in various experimental models because of low expression of MHC class I and II as well as co-stimulatory molecules. We investigated the immunogenicity of a human NSC line grown in normal culture conditions and in the pr...