From binary labels to dynamic landscapes: The evolving computational prediction of protein-RNA interactions through tasks and deep learning paradigms.
This study reviews the methodological evolution from conventional machine learning to deep learning, graph neural networks and large-scale pre-trained language models, and compares their differences in data preparation, evaluation protocols and generalization behavior, and places particular emphasis on recent advances in structure-aware and condition-aware models.