A Genetic Algorithm-based Weighted Ensemble Learning For Predicting PiRNAs

Predicting piwi-interacting RNA(piRNA)is an important topic in the small non-coding RNAs,which provides clues for understanding the generation mechanism of gamete.To the best of our knowledge,several machine learning approaches have been proposed for the piRNA prediction,but there is still room for improvements.In this paper,we develop a genetic algorithm-based weighted ensemble learning for predicting piRNAs.We construct datasets for three species:Human,Mouse and Drosophila.For each species,we compile the balanced dataset and imbalanced dataset,and thus obtain six datasets to build and evaluate prediction models.In the computational experiments,we adopt 10-fold cross validation as evaluation.The genetic algorithm-based weighted ensemble learning achieves AUC of 0.932,0.937 and 0.995 on the balanced Human dataset,Mouse dataset and Drosophila dataset,respectively,and achieves AUC of 0.935,0.939 and 0.996 on the imbalanced datasets of three species.Further,we use the prediction models trained on the Mouse dataset to identify piRNAs of other species,and the models demonstrate the good performances in the cross-species prediction.Compared with other state-of-the-art methods,our method can lead to better performances as well as good robustness.In conclusion,the proposed method is promising for piRNA prediction.The source codes and datasets are available in https://github.com/luolongqiang/piRNA-prediction.