From binary labels to dynamic landscapes: The evolving computational prediction of protein-RNA interactions through tasks and deep learning paradigms.

Computational prediction of protein-RNA interactions has evolved from conventional machine learning to deep learning, graph neural networks, and large-scale pre-trained language models across 2010–2025.

• Why it matters: The field needs clearer guidance on how task framing, data preparation, and evaluation protocols affect generalization, especially for structure-aware, condition-aware, and low-data settings.
• What they did: A task-oriented classification review groups RPI methods into five categories—RBP classification, RPI prediction, RNA binding site/profile modeling, residue-level interface prediction, and quantitative affinity/mutation estimation—then compares data, evaluation, and generalization.
• The result: The review provides benchmarking recommendations and points to integrating spatial omics and developing dynamic, generative landscapes of RPIs to better support biomedical research.

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