The interactions between RNAs and proteins play critical roles in many biological
processes. Therefore, characterizing these interactions becomes critical for mechanistic, biomedical,
and clinical studies. Many experimental methods can be used to determine RNA-protein
interactions in multiple aspects. However, due to the facts that RNA-protein interactions are tissuespecific
and condition-specific, as well as these interactions are weak and frequently compete with
each other, those experimental techniques can not be made full use of to discover the complete
spectrum of RNA-protein interactions. To moderate these issues, continuous efforts have been
devoted to developing high quality computational techniques to study the interactions between
RNAs and proteins. Many important progresses have been achieved with the application of novel
techniques and strategies, such as machine learning techniques. Especially, with the development
and application of CLIP techniques, more and more experimental data on RNA-protein interaction
under specific biological conditions are available. These CLIP data altogether provide a rich source
for developing advanced machine learning predictors. In this review, recent progresses on
computational predictors for RNA-protein interaction were summarized in the following aspects:
dataset, prediction strategies, and input features. Possible future developments were also discussed
at the end of the review.
Keywords: RNA-protein interaction, RNA-binding protein, RNA-binding domain, RNA-binding motif, RNA-binding residue,
protein-binding nucleotide, machine learning, deep learning, meta-strategy, UniProt, PDB, CLIP, sequence feature, structural
feature, physicochemical feature, evolutionary information, PSSM.
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