idenLD-AREL: identifying lncRNA-disease associations by random forests based on an ensemble learning framework

Identification of disease-associated long non-coding RNAs (lncRNAs) facilitates the understanding of the pathogenesis of complex diseases. Many different types of computational models have been proposed. Although some of them have achieved encouraging results in predicting disease-associated lncRNAs, how to obtain stable results is still a challenge. In this paper, we propose a computational model based on an ensemble learning framework via the adaptive random forests, in short, idenLD-AREL. The idenLD-AREL integrates multiple random forest predictors and adaptive strategies to predict the scores of potential lncRNA-disease associations (LDAs), which ensure the stability and accuracy of the prediction results. In addition, there are a large number of false negative samples in the association datasets. For this reason, the resampling strategy is applied to idenLD-AREL to balance the samples. The idenLD-AREL is assessed by five-fold cross-validation in both the benchmark dataset and independent test set, showing excellent performance. Besides, the experimental results of the case study further demonstrate the effectiveness of the idenLD-AREL in predicting potential LDAs. The demo codes of the iLncDA-RSN are available online at https://github.com/CDMBlab/idenLD-AREL.