ReviewFrontiers in molecular biosciences2022
Deep Learning in RNA Structure Studies.
Review in Frontiers in molecular biosciences, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 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.
The trial behind it
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Who cites it
16 citing papers in PubMed, 44 citations in OpenAlex.
- AI foundation models for RNA biology.RNA biology · 2026Review
- The Use of Deep Learning in RNA Therapeutic Development.ACS nano · 2026Review
- Gaining insights into epigenetic memories through artificial intelligence and omics science in plants.Journal of integrative plant biology · 2025Review
- How large is the universe of RNA-like motifs? A clustering analysis of RNA graph motifs using topological descriptors.PLoS computational biology · 2025Article
- GINClus: RNA structural motif clustering using graph isomorphism network.NAR genomics and bioinformatics · 2025Article
- AI-Driven TENGs for Self-Powered Smart Sensors and Intelligent Devices.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Review
- Systematic benchmarking of deep-learning methods for tertiary RNA structure prediction.PLoS computational biology · 2024Article
- Identification of RNA structures and their roles in RNA functions.Nature reviews. Molecular cell biology · 2024Review
- The role of secondary structures in the functioning of 3' untranslated regions of mRNA: A review of functions of 3' UTRs' secondary structures and hypothetical involvement of secondary structures in cytoplasmic polyadenylation in Drosophila.BioEssays : news and reviews in molecular, cellular and developmental biology · 2024Review
- Tailor made: the art of therapeutic mRNA design.Nature reviews. Drug discovery · 2024Review
- Personalized cancer vaccine design using AI-powered technologies.Frontiers in immunology · 2024Review
- A Hitchhiker's guide to RNA-RNA structure and interaction prediction tools.Briefings in bioinformatics · 2023Review
- Article
- High-throughput biochemistry in RNA sequence space: predicting structure and function.Nature reviews. Genetics · 2023Review
- G4Atlas: a comprehensive transcriptome-wide G-quadruplex database.Nucleic acids research · 2023Article
- Computational Pipeline for Reference-Free Comparative Analysis of RNA 3D Structures Applied to SARS-CoV-2 UTR Models.International journal of molecular sciences · 2022Article
Corrections and comments
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Authors and funding
3 authors at 2 institutions in 1 country.
Funding
Abstract
Deep learning, or artificial neural networks, is a type of machine learning algorithm that can decipher underlying relationships from large volumes of data and has been successfully applied to solve structural biology questions, such as RNA structure. RNA can fold into complex RNA structures by forming hydrogen bonds, thereby playing an essential role in biological processes. While experimental effort has enabled resolving RNA structure at the genome-wide scale, deep learning has been more recently introduced for studying RNA structure and its functionality. Here, we discuss successful applications of deep learning to solve RNA problems, including predictions of RNA structures, non-canonical G-quadruplex, RNA-protein interactions and RNA switches. Following these cases, we give a general guide to deep learning for solving RNA structure problems.
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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.