ArticleBMC bioinformatics2021
ReCGBM: a gradient boosting-based method for predicting human dicer cleavage sites.
Article in BMC bioinformatics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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4 citing papers in PubMed, 9 citations in OpenAlex.
- Leveraging artificial intelligence in antibody-drug conjugate development: from target identification to clinical translation in oncology.NPJ precision oncology · 2025Review
- Insights into the evolution and regulation of miRNAs from the view of their DNA replication temporal domains.Frontiers in genetics · 2025Article
- DiCleave: a deep learning model for predicting human Dicer cleavage sites.BMC bioinformatics · 2024Article
- Staem5: A novel computational approachfor accurate prediction of m5C site.Molecular therapy. Nucleic acids · 2021Article
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Authors and funding
4 authors at 3 institutions in 3 countries.
Funding
Abstract
backgroundHuman dicer is an enzyme that cleaves pre-miRNAs into miRNAs. Several models have been developed to predict human dicer cleavage sites, including PHDCleav and LBSizeCleav. Given an input sequence, these models can predict whether the sequence contains a cleavage site. However, these models only consider each sequence independently and lack interpretability. Therefore, it is necessary to develop an accurate and explainable predictor, which employs relations between different sequences, to enhance the understanding of the mechanism by which human dicer cleaves pre-miRNA.
resultsIn this study, we develop an accurate and explainable predictor for human dicer cleavage site - ReCGBM. We design relational features and class features as inputs to a lightGBM model. Computational experiments show that ReCGBM achieves the best performance compared to the existing methods. Further, we find that features in close proximity to the center of pre-miRNA are more important and make a significant contribution to the performance improvement of the developed method.
conclusionsThe results of this study show that ReCGBM is an interpretable and accurate predictor. Besides, the analyses of feature importance show that it might be of particular interest to consider more informative features close to the center of the pre-miRNA in future predictors.
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