LncPred-IEL: A Long Non-coding RNA Prediction Method using Iterative Ensemble Learning

A large number of transcripts have been generated by the development of high throughput sequencing technologies. Predicting lncRNA from transcripts is a challenging and important task. In this paper, we propose LncPred-IEL, an iterative ensemble learning long non-coding RNA prediction method. LncPred-IEL not only considers features widely used for the lncRNA prediction, but also take into account sequence-derived features used in the RNA sequence classification, so as to make use of diverse information. LncPred-IEL builds base predictors based on different groups of features, and employs a supervised iterative way to combine base predictors and build ensemble models. Our studies demonstrate that supervised iterative way can learn the representations that help to separate lncRNA and protein-coding transcripts, and further improve the performances. Experiments demonstrate that LncPred-IEL outperforms several state-of-the-art methods when evaluated by 10-fold cross-validation. The capability of LncPred-IEL for the cross-species prediction is also tested. As complementary to wet experiments, LncPred-IEL is a useful computational tool for lncRNA prediction.

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