BACKGROUND microRNAs (miRNAs) are short regulatory RNAs that are involved in several diseases, including cancers. Identifying miRNA functions is very important in understanding disease mechanisms and determining the efficacy of drugs. An increasing number of computational methods have been developed to explore miRNA functions by inferring the miRNA-mRNA regulatory relationships from data. Each ...

Anti-Friction Bearing (AFB) is a very important machine component and its unscheduled failure leads to cause of malfunction in wide range of rotating machinery which results in unexpected downtime and economic loss. In this paper, ensemble machine learning techniques are demonstrated for the detection of different AFB faults. Initially, statistical features were extracted from temporal vibratio...

The Dynamical–Statistical–Analog Ensemble Forecast model for landfalling typhoon precipitation (the DSAEF_LTP model) identifies tropical cyclones (TCs) from history data that are similar to a target TC and then assembles the amounts distributions of those identified obtain TC. Two original ensemble methods in model, namely, mean maximum, tend under- over- forecast precipitation, respectively. I...

Ensemble methods, which combine several classifiers, have been successfully applied to decrease generalization error of machine learning methods. For most ensemble methods the ensemble members are combined by weighted summation of the output, called the linear average predictor. The logarithmic opinion pool ensemble method uses a multiplicative combination of the ensemble members, which treats ...

Quantitative structure-activity relationship (QSAR) is a computer modeling technique for identifying relationships between the structural properties of chemical compounds and biological activity. QSAR necessary drug discovery, but it has many limitations. Ensemble-based machine learning approaches have been used to overcome limitations generate reliable predictions. Ensemble creates set diverse...