Ribonucleic-Acid Protein Interaction Prediction Based on Deep Learning: A Comprehensive Survey

[2410.00077] Ribonucleic-Acid Protein Interaction Prediction Based on Deep Learning: A Comprehensive Survey Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Quantitative Biology > Quantitative Methods arXiv:2410.00077 (q-bio) [Submitted on 30 Sep 2024 ( v1 ), last revised 15 Aug 2026 (this version, v2)] Title: Ribonucleic-Acid Protein Interaction Prediction Based on Deep Learning: A Comprehensive Survey Authors: Danyu Li , Rubing Huang , Chenhui Cui , Dave Towey , Ling Zhou , Jinyu Tian , Bin Zou View a PDF of the paper titled Ribonucleic-Acid Protein Interaction Prediction Based on Deep Learning: A Comprehensive Survey, by Danyu Li and 6 other authors View PDF HTML (experimental) Abstract: The interaction between Ribonucleic Acids (RNAs) and proteins, also called RNA Protein Interaction (RPI), governs biological processes, including gene regulation and disease pathogenesis. This comprehensive survey examines Artificial Intelligence (AI) applications in Deep Learning-based RPI Prediction (DL-based RPIP) through eight Research Questions (RQs), analyzing 179 studies (2014--2023). The key findings include: sustained technical evolution through embryonic (2014--2017), accelerated (2018--2022), and expansion phases (2023) (RQ1); hybrid models integrating Graph Neural Networks (GNNs) (for topological interface modeling) and Transformers (for long-range dependencies) achieve state-of-the-art performance (RQ4); pretrained language models enhance small-sample learning, but the cross-species generalization declines sharply with evolutionary distance (RQ5). Critical challenges persist, including data heterogeneity across databases, the scarcity of standardized benchmarks (RQ2), and balancing the trade-off between feature encoding and information preservation (RQ3). Future advancements require biologically informed DL architectures, multi-feature fusion, and rigorous cross-validation to bridge the generalization-interpretability gap (RQ8): This would accelerate the clinical translation of predictive tools (RQ6/RQ7). As the first comprehensive analysis spanning feature encoding, modeling, evaluation, applications, and tools, this work fills a critical gap in the DL-based RPIP literature. Comments: Accepted for publication in Applied Soft Computing. DOI: https://doi.org/10.1016/j.asoc.2025.113795 Subjects: Quantitative Methods (q-bio.QM) Cite as: arXiv:2410.00077 [q-bio.QM] (or arXiv:2410.00077v2 [q-bio.QM] for this version) https://doi.org/10.48550/arXiv.2410.00077 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Chenhui Cui [ view email ] [v1] Mon, 30 Sep 2024 16:09:55 UTC (2,301 KB) [v2] Sat, 15 Aug 2026 09:10:40 UTC (3,224 KB) Full-text links: Access Paper: View a PDF of the paper titled Ribonucleic-Acid Protein Interaction Prediction Based on Deep Learning: A Comprehensive Survey, by Danyu Li and 6 other authors View PDF HTML (experimental) TeX Source view license Current browse context: q-bio.QM < prev | next > new | recent | 2024-10 Change to browse by: q-bio References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... 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