ArticleBriefings in bioinformatics2023
A systematic benchmark of machine learning methods for protein-RNA interaction prediction.
Article in Briefings in bioinformatics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
- Review
- NPBIP: predicting binding preferences of uncharacterized nucleic-acid-binding proteins.Bioinformatics (Oxford, England) · 2026Article
- Accessibility in proteins and RNAs interactions prediction with machine learning: are we overlooking non-experts?Briefings in bioinformatics · 2026Review
- Augmented prediction of multi-species protein-RNA interactions using evolutionary conservation of RNA-binding proteins.Nature communications · 2026Article
- Sequence-Based Models for RNA-Protein Interactions Imputation Might Be Insufficient for Novel Signal Prediction in eCLIP Data.International journal of molecular sciences · 2026Article
- Overview of exosomal non-coding RNAs in cardiovascular disease using high throughput sequencing.European journal of pharmacology · 2025Review
- diPaRIS: Dynamic and Interpretable Protein-RNA Interactions Prediction With U-Shaped Network and Novel Structure Encoding.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Base-resolution binding profile prediction of proteins on RNAs with deep learning.Nucleic acids research · 2025Article
- LncPTPred: predicting lncRNA-protein interaction based on crosslinking and immunoprecipitation (CLIP-Seq) data.Briefings in bioinformatics · 2025Article
- RBPsuite 2.0: an updated RNA-protein binding site prediction suite with high coverage on species and proteins based on deep learning.BMC biology · 2025Article
- A new perspective on microRNA-guided gene regulation specificity, and its potential generalization to transcription factors and RNA-binding proteins.Nucleic acids research · 2024Review
- Big data and deep learning for RNA biology.Experimental & molecular medicine · 2024Review
- Review
- PB-LKS: a python package for predicting phage-bacteria interaction through local K-mer strategy.Briefings in bioinformatics · 2024Article
- 'Artificial intelligence and machine learning in RNA biology'.Briefings in bioinformatics · 2023Article
Corrections and comments
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Authors and funding
8 authors.
Funding
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Abstract
RNA-binding proteins (RBPs) are central actors of RNA post-transcriptional regulation. Experiments to profile-binding sites of RBPs in vivo are limited to transcripts expressed in the experimental cell type, creating the need for computational methods to infer missing binding information. While numerous machine-learning based methods have been developed for this task, their use of heterogeneous training and evaluation datasets across different sets of RBPs and CLIP-seq protocols makes a direct comparison of their performance difficult. Here, we compile a set of 37 machine learning (primarily deep learning) methods for in vivo RBP-RNA interaction prediction and systematically benchmark a subset of 11 representative methods across hundreds of CLIP-seq datasets and RBPs. Using homogenized sample pre-processing and two negative-class sample generation strategies, we evaluate methods in terms of predictive performance and assess the impact of neural network architectures and input modalities on model performance. We believe that this study will not only enable researchers to choose the optimal prediction method for their tasks at hand, but also aid method developers in developing novel, high-performing methods by introducing a standardized framework for their evaluation.
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