ArticlePloS one2025
rbpTransformer: A novel deep learning model for prediction of piRNA and mRNA bindings.
Article in PloS one, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
3 citing papers in PubMed.
- rbpCNN: a biophysics-informed deep learning model for predicting piRNA and mRNA interactions.Scientific reports · 2026Article
- Systemic immune-inflammation (SII) index as a novel prognostic biomarker in critically ill patients with sepsis: analysis of the MIMIC-IV cohort and predictive modeling based on machine learning.BMC infectious diseases · 2026Article
- Stress hyperglycemia ratio improves machine learning-based mortality risk prediction in critically ill COVID-19 patients: a multicenter retrospective study.BMC infectious diseases · 2026Article
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Authors and funding
1 author.
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Abstract
An important issue in biotechnology is predicting whether a piRNA and an mRNA will or will not bind. Research and treatment of diseases, drug discovery, and the silencing and regulation of genes, transposons, and genomic stability may all benefit from accurate binding predictions. The literature offers numerous deep-learning models for piRNA and mRNA binding prediction. However, a proper adjustment of the effective transformer model and the impact of important design alternatives has not been evaluated thoroughly. This paper summarizes the models available in the literature, briefly introduces transformers, then offers a novel deep learning model and evaluates various design alternatives, including k-mer size, number of core modules, choice of optimization algorithm, and whether to use self-attention. The results show that rbpTransformer can be a good candidate for building deep AI models to predict the binding of piRNA and mRNA sequences with an AUC value of 94.38%. The test results also reveal how the design affects the model's accuracy.
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Registered trials
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