background: data mining (dm) is an approach used in extracting valuable information from environmental processes. this research depicts a dm approach used in extracting some information from influent and effluent wastewater characteristic data of a waste stabilization pond (wsp) in birjand, a city in eastern iran. methods: multiple regression (mr) and neural network (nn) models were examined us...

MOTIVATION Despite advances in the gene annotation process, the functions of a large portion of gene products remain insufficiently characterized. In addition, the in silico prediction of novel Gene Ontology (GO) annotations for partially characterized gene functions or processes is highly dependent on reverse genetic or functional genomic approaches. To our knowledge, no prediction method has ...

In this paper, we focus on the new model selection procedure of the discriminant analysis. Combining resampling technique with k-fold cross validation, we develop a k-fold cross validation for small sample method. By this breakthrough, we obtain the mean error rate in the validation samples (M2) and the 95% confidence interval (CI) of discriminant coefficient. Moreover, we propose the model sel...

Cholestasis represents one out of three types of drug induced liver injury (DILI), which comprises a major challenge in drug development. In this study we applied a two-class classification scheme based on k-nearest neighbors in order to predict cholestasis, using a set of 93 two-dimensional (2D) physicochemical descriptors and predictions of selected hepatic transporters' inhibition (BSEP, BCR...

Landslides have been a regular occurrence and an alarming threat to human life property in the era of anthropogenic global warming. An early prediction landslide susceptibility using data-driven approach is demand time. In this study, we explored eloquent features that best describe with state-of-the-art machine learning methods. our employed algorithms including XgBoost, LR, KNN, SVM, Adaboost...

Objectives: To propose a Bagging ensemble method to predict heart disease at early stages. The main focus of this research is increase the prediction accuracy in model. Methods: proposed system experimented with by using Cleveland datasets collected from UCI repository. dataset consists 14 attributes. In we applied different machine learning algorithms such as Decision tree, Naïve Bayes, Random...

MicroRNAs (miRNAs) are small, noncoding regulatory molecules. They are involved in many essential biological processes and act by suppressing gene expression. The present work reports an integrative analysis of miRNA-mRNA and miRNA-miRNA interactions and their regulatory patterns using high-throughput miRNA and mRNA datasets. Aberrantly expressed miRNA and mRNA profiles were obtained based on f...

Abstract Wind energies are one of the most used resources worldwide and favours economy by not emitting harmful gases that could lead to global warming. It is a cost-efficient method environmentally friendly. Hence, explains popularity wind energy production over years. Unfortunately, minor fault be contagious affecting nearby components, then more complicated problem might arise, which may cos...