ArticleGenome research2020
Deep neural networks for interpreting RNA-binding protein target preferences.
Article in Genome research, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 63 papers, 1 of them a synthesis that pooled it.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
63 citing papers in PubMed, 1 synthesis or guideline pooled it, 124 citations in OpenAlex.
- The genetic and biochemical determinants of mRNA degradation rates in mammals.Genome biology · 2022Pooled it
- Spatial mapping of RNA turnover kinetics in the mouse brain.Nature neuroscience · 2026Article
- Review
- A novel NLP-based method and algorithm to discover RNA-binding protein (RBP) motifs, contexts, binding preferences, and interactions.RNA (New York, N.Y.) · 2026Article
- Bridging sequence-structure motifs and genetic variants for genome-wide dynamic RNA-protein interaction profiling.Nature communications · 2026Article
- Signal or noise? RNA-binding proteins and the challenges of binding site assignments.Nucleic acids research · 2026Review
- Allele-specific splicing modulates protein isoforms and Alzheimer's risk.bioRxiv : the preprint server for biology · 2026Article
- Allele-specific alternative polyadenylation links noncoding genetic variation to Alzheimer's disease risk.bioRxiv : the preprint server for biology · 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
- EIciRNAs in focus: current understanding and future perspectives.RNA biology · 2025Review
- Deciphering RNA modification and post-transcriptional regulation with NetRNApan.Briefings in bioinformatics · 2025Article
- Long-read RNA-seq demarcates cis- and trans-directed alternative RNA splicing.Nature communications · 2025Article
- diPaRIS: Dynamic and Interpretable Protein-RNA Interactions Prediction With U-Shaped Network and Novel Structure Encoding.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- deepTFBS: Improving within- and Cross-Species Prediction of Transcription Factor Binding Using Deep Multi-Task and Transfer Learning.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Explainable AI in Genomics: Transcription Factor Binding Site Prediction with Mixture of Experts.ArXiv · 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
- From computational models of the splicing code to regulatory mechanisms and therapeutic implications.Nature reviews. Genetics · 2025Review
- Emerging RNA-centric technologies to probe RNA-protein interactions: importance in decoding the life cycle of positive sense single strand RNA viruses and antiviral discovery.Frontiers in cellular and infection microbiology · 2025Review
- EnrichRBP: an automated and interpretable computational platform for predicting and analysing RNA-binding protein events.Bioinformatics (Oxford, England) · 2024Article
- A combined deep learning framework for mammalian m6A site prediction.Cell genomics · 2024Article
3 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Deep learning has become a powerful paradigm to analyze the binding sites of regulatory factors including RNA-binding proteins (RBPs), owing to its strength to learn complex features from possibly multiple sources of raw data. However, the interpretability of these models, which is crucial to improve our understanding of RBP binding preferences and functions, has not yet been investigated in significant detail. We have designed a multitask and multimodal deep neural network for characterizing in vivo RBP targets. The model incorporates not only the sequence but also the region type of the binding sites as input, which helps the model to boost the prediction performance. To interpret the model, we quantified the contribution of the input features to the predictive score of each RBP. Learning across multiple RBPs at once, we are able to avoid experimental biases and to identify the RNA sequence motifs and transcript context patterns that are the most important for the predictions of each individual RBP. Our findings are consistent with known motifs and binding behaviors and can provide new insights about the regulatory functions of RBPs.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.