title: A genetic algorithm-based weighted ensemble method for predicting transposon-derived piRNAs.
abstract: BACKGROUND
  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.
  
  
  RESULTS
  In this paper, we develop a genetic algorithm-based weighted ensemble method for predicting transposon-derived 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, the genetic algorithm-based weighted ensemble method achieves 10-fold cross validation 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.
  
  
  CONCLUSIONS
  Compared with other state-of-the-art methods, our method can lead to better performances. In conclusion, the proposed method is promising for the transposon-derived piRNA prediction. The source codes and datasets are available in https://github.com/zw9977129/piRNAPredictor .
authors: School of Mathematics and Statistics, Wuhan University, Wuhan, 430072, China.
authors: Dingfang
authors: D
authors: Li
authors: School of Mathematics and Statistics, Wuhan University, Wuhan, 430072, China.
authors: Longqiang
authors: L
authors: Luo
authors: State Key Lab of Software Engineering, Wuhan University, Wuhan, 430072, China. zhangwen@whu.edu.cn.|School of Computer, Wuhan University, Wuhan, 430072, China. zhangwen@whu.edu.cn.
authors: Wen
authors: W
authors: Zhang
authors: International School of Software, Wuhan University, Wuhan, 430072, China.
authors: Feng
authors: F
authors: Liu
authors: State Key Lab of Software Engineering, Wuhan University, Wuhan, 430072, China.|School of Computer, Wuhan University, Wuhan, 430072, China.
authors: Fei
authors: F
authors: Luo
mesh_terms: D000465:Algorithms; D000818:Animals; D019540:Area Under Curve; D004251:DNA Transposable Elements / Q000235:genetics*; D004330:Drosophila / Q000235:genetics; D006801:Humans; D051379:Mice; D034741:RNA, Small Interfering / Q000235:genetics / Q000378:metabolism*; D012372:ROC Curve; D014584:User-Computer Interface
chemical_list: D004251:DNA Transposable Elements; D034741:RNA, Small Interfering
keywords: Ensemble learning; Feature; Genetic algorithm; piRNA