ArticleBMC bioinformatics2016
A genetic algorithm-based weighted ensemble method for predicting transposon-derived piRNAs.
Article in BMC bioinformatics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 40 papers, 1 of them a synthesis that pooled it.
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Who cites it
40 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Advanced artificial intelligence in piRNA and PIWI-like protein research: A systematic review of recurrent neural networks, long short-term memory, and emerging computational techniquesBiomedica : revista del Instituto Nacional de Salud · 2025Pooled it
- MiSiPi.Rna: an integrated tool for characterizing small regulatory RNA processing.RNA (New York, N.Y.) · 2026Article
- Databases and computational methods for the identification of piRNA-related molecules: A survey.Computational and structural biotechnology journal · 2024Review
- Guided optimization of ToxPi model weights using a Semi-Automated approach.Computational toxicology (Amsterdam, Netherlands) · 2024Article
- piRNA-disease association prediction based on multi-channel graph variational autoencoder.PeerJ. Computer science · 2024Article
- LSTM4piRNA: Efficient piRNA Detection in Large-Scale Genome Databases Using a Deep Learning-Based LSTM Network.International journal of molecular sciences · 2023Article
- COVID-19 mortality prediction using ensemble learning and grey wolf optimization.PeerJ. Computer science · 2023Article
- Stacking-ac4C: an ensemble model using mixed features for identifying n4-acetylcytidine in mRNA.Frontiers in immunology · 2023Article
- Implementation of ensemble machine learning algorithms on exome datasets for predicting early diagnosis of cancers.BMC bioinformatics · 2022Article
- A Review of Discovery Profiling of PIWI-Interacting RNAs and Their Diverse Functions in Metazoans.International journal of molecular sciences · 2021Review
- Bioinformatics and Machine Learning Approaches to Understand the Regulation of Mobile Genetic Elements.Biology · 2021Review
- Advances in Computational Methodologies for Classification and Sub-Cellular Locality Prediction of Non-Coding RNAs.International journal of molecular sciences · 2021Review
- The biogenesis and biological function of PIWI-interacting RNA in cancer.Journal of hematology & oncology · 2021Review
- Quantitative Toxicity Prediction via Meta Ensembling of Multitask Deep Learning Models.ACS omega · 2021Article
- Emerging Classes of Small Non-Coding RNAs With Potential Implications in Diabetes and Associated Metabolic Disorders.Frontiers in endocrinology · 2021Review
- Use Chou's 5-steps rule to identify DNase I hypersensitive sites via dinucleotide property matrix and extreme gradient boosting.Molecular genetics and genomics : MGG · 2020Article
- 2lpiRNApred: a two-layered integrated algorithm for identifying piRNAs and their functions based on LFE-GM feature selection.RNA biology · 2020Article
- Mobile genomics: tools and techniques for tackling transposons.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2020Review
- Is There Any Sequence Feature in the RNA Pseudouridine Modification Prediction Problem?Molecular therapy. Nucleic acids · 2020Article
- LncRNA-miRNA interaction prediction through sequence-derived linear neighborhood propagation method with information combination.BMC genomics · 2019Article
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Authors and funding
5 authors.
Funding
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Abstract
backgroundPredicting 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.
resultsIn 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.
conclusionsCompared 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 .
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