ArticleCell research2021
Predicting dynamic cellular protein-RNA interactions by deep learning using in vivo RNA structures.
Article in Cell research, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 81 papers.
What it found
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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.
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Who cites it
81 citing papers in PubMed, 132 citations in OpenAlex.
- dsRNAscan maps human dsRNAome, revealing conservation, intermolecular dsRNA, and correlates of ADAR dependency.Molecular cell · 2026Article
- ProRB: a structure-free unified framework for joint prediction and design of protein-RNA interactions.Nucleic acids research · 2026Article
- Review
- Structure-aware deep learning enhances m6A prediction and reveals cell type-associated RNA structural signatures.PLoS computational biology · 2026Article
- K-attention: a biologically informed attention operator for data-efficient sequence-based omics modeling.Briefings in bioinformatics · 2026Article
- NAT10 maintains stem cell homeostasis by mitigating mRNA decay through an acNature cell biology · 2026Article
- Bridging technical innovation and computational advances in studies of RNA-protein assemblies.Nature reviews. Genetics · 2026Review
- 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
- DNA-guided CRISPR-Cas12 for cellular RNA targeting.Nature biotechnology · 2026Article
- Bridging sequence-structure motifs and genetic variants for genome-wide dynamic RNA-protein interaction profiling.Nature communications · 2026Article
- Accessibility in proteins and RNAs interactions prediction with machine learning: are we overlooking non-experts?Briefings in bioinformatics · 2026Review
- IGF2BP3 promotes immune evasion and predicts poor prognosis in head and neck squamous cell carcinoma.Translational cancer research · 2026Article
- Augmented prediction of multi-species protein-RNA interactions using evolutionary conservation of RNA-binding proteins.Nature communications · 2026Article
- R-loops orchestrate RNAPII transcriptional reprogramming for the maternal-to-zygotic transition.Cell research · 2026Article
- Decoding Protein-Membrane Binding Interfaces from Surface-Fingerprint-Based Geometric Deep Learning and Molecular Dynamics Simulations.Journal of chemical information and modeling · 2026Article
- PRIME-BSPre: a genome-wide protein-RNA binding sites prediction method based on templates.BMC genomics · 2026Article
- Structural probing of RNA hairpins quantifies protein occupancy on RNA and links it to function in human cells.bioRxiv : the preprint server for biology · 2026Article
- Predicting small molecule-RNA interactions without RNA tertiary structures.Nature biotechnology · 2026Article
- Integrative analysis of RNA binding proteins identifies DDX55 as a novel regulator of 3'UTR isoform diversity.Genome biology · 2025Article
- Biomolecular Interaction Prediction: The Era of AI.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Review
21 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
8 authors at 3 institutions in 2 countries.
Funding
Abstract
Interactions with RNA-binding proteins (RBPs) are integral to RNA function and cellular regulation, and dynamically reflect specific cellular conditions. However, presently available tools for predicting RBP-RNA interactions employ RNA sequence and/or predicted RNA structures, and therefore do not capture their condition-dependent nature. Here, after profiling transcriptome-wide in vivo RNA secondary structures in seven cell types, we developed PrismNet, a deep learning tool that integrates experimental in vivo RNA structure data and RBP binding data for matched cells to accurately predict dynamic RBP binding in various cellular conditions. PrismNet results for 168 RBPs support its utility for both understanding CLIP-seq results and largely extending such interaction data to accurately analyze additional cell types. Further, PrismNet employs an "attention" strategy to computationally identify exact RBP-binding nucleotides, and we discovered enrichment among dynamic RBP-binding sites for structure-changing variants (riboSNitches), which can link genetic diseases with dysregulated RBP bindings. Our rich profiling data and deep learning-based prediction tool provide access to a previously inaccessible layer of cell-type-specific RBP-RNA interactions, with clear utility for understanding and treating human diseases.
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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.