ArticleNucleic acids research2025
Enhanced RNA secondary structure prediction through integrative deep learning and structural context analysis.
Article in Nucleic acids research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.
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Who cites it
26 citing papers in PubMed.
- Integrated morpho-biochemical, molecular, phylogenetic, and thermodynamic characterization of Bacillus isolates from ecologically distinct Indian soils.Molecular biology reports · 2026Article
- UFold-X: an enhanced Dual & Dynamic U-Mamba model for long-range RNA secondary structure prediction.Nucleic acids research · 2026Article
- Predicting circRNA-Drug Sensitivity Using Integrated Hybrid Graph and Molecular Features.Interdisciplinary sciences, computational life sciences · 2026Article
- HPCSMN: A Classification Method of Chemotherapy Sensitivity of Hypopharyngeal Cancer Based on Multimodal Network.Interdisciplinary sciences, computational life sciences · 2026Article
- The next paradigm in bioinformatics: a review of multi-agent systems and foundational models for end-to-end scientific discovery.Briefings in bioinformatics · 2026Review
- rbpCNN: a biophysics-informed deep learning model for predicting piRNA and mRNA interactions.Scientific reports · 2026Article
- HiGLDP: a hierarchical graph neural network for predicting lncRNA-disease associations through multi-omic integration.BMC biology · 2026Article
- From Synthesis to Application: Functionalized Magnetic Nanoparticles as a Simple and Reliable Tool for Nucleic Acid Purification.ACS omega · 2026Article
- NTFold: Structure-Sensing Nucleotide Attention Learning for RNA Secondary Structure Prediction.Sensors (Basel, Switzerland) · 2026Article
- Integrating Genomics, Radiomics, and Pathomics in Oncology: A Scoping Review and a Framework for AI-Enabled Surgomics.Bioengineering (Basel, Switzerland) · 2026Review
- S2DA-GO: enhancing protein function prediction via gradient-decoupled cross-attention and semantic priors.Frontiers in genetics · 2026Article
- Leveraging blockchain with zero knowledge proofs in wearable health technologies for personalized healthcare.Scientific reports · 2025Article
- Optimizing sustainable pore design in MIL-125(Ti) for biomedicine using morphological tuning informed by Gaussian, Lorentzian, and Boltzmann fitting functions.Communications chemistry · 2025Article
- Alternative Splicing: Molecular Mechanisms, Biological Functions, Diseases, and Potential Therapeutic Targets.MedComm · 2025Review
- The intersection of ferroptosis and non-coding RNAs: a novel approach to lung cancer.Irish journal of medical science · 2025Review
- Double-strand break-free and transgene-free genome editing in the microalga Nannochloropsis oceanica using removable vectors containing the CRISPR base editing system.Scientific reports · 2025Article
- Cutting-edge optimized multi-source data fusion for trusted execution and management of blockchain transactions on the internet of medical things (IoMT) with machine learning.Scientific reports · 2025Article
- Hypergraph Learning with Hyperedge Gating and Multiscale Topology Feature Learning for Predicting Disease-Related circRNAs.ACS omega · 2025Article
- Co-allosteric hairpin probe for detecting microRNA with high specificity.RSC advances · 2025Article
- Prognostic signature and tumor microenvironment infiltration based on amino acid metabolism-related genes and lncRNAs of colorectal cancer.Scientific reports · 2025Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
Analyzing RNA secondary structures plays a crucial role in elucidating the functional mechanisms of RNA. Despite advances in RNA structure determination, these methods are low throughout and resource-intensive. While machine learning-based models have achieved remarkable performance in terms of prediction accuracy, challenges such as data scarcity and overfitting remain common. Here, we introduce a phased learning strategy that integrates RNA sequence and structural context information to mitigate the risk of overfitting and employs pairing constraints to train the model on folding scores. This approach effectively addresses both local and long-range nucleotide interactions, substantially improving the robustness of RNA secondary structure predictions. Our comprehensive analysis across multiple benchmarking datasets demonstrated that the performance of our model (DSRNAFold) was superior to that of existing methods, especially in pseudoknot recognition and chemical mapping activity prediction, where our approach showed positive performance.
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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.