Research progress on prediction of RNA-protein binding sites in the past five years.

Accurately predicting RNA-protein binding sites is essential to gain a deeper comprehension of the protein-RNA interactions and their regulatory mechanisms, which are fundamental in gene expression and regulation. However, conventional biological approaches to detect these sites are often costly and time-consuming. In contrast, computational methods for predicting RNA protein binding sites are both cost-effective and expeditious. This review synthesizes already existing computational methods, summarizing commonly used databases for predicting RNA protein binding sites. In addition, applications and innovations of computational methods using traditional machine learning and deep learning for RNA protein binding site prediction during 2018–2023 are presented. These methods cover a wide range of aspects such as effective database utilization, feature selection and encoding, innovative classification algorithms, and evaluation strategies. Exploring the limitations of existing computational methods, this paper delves into the potential directions for future development. DeepRKE, RDense, and DeepDW all employ convolutional neural networks and long and short-term memory networks to construct prediction models, yet their algorithm design and feature encoding differ, resulting in diverse prediction performances. [Display omitted] • Help researchers better understand RNA-binding proteins and associated mechanisms. • Using computational methods to predict related sites is less costly and faster. • Summarize databases, present the applications and innovations from 2018 to 2022. • Compare differences of models in the design of algorithms and feature extracted. • Discuss the shortcomings and potential directions for future development. [ABSTRACT FROM AUTHOR]