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Deep Learning for RNA Secondary Structure Determination: Gauging Generalizability and Broadening the Scope of Traditional Methods

bioRxiv (Cold Spring Harbor Laboratory)(2025)10.1101/2025.11.04.686644Source: DataRank Database

Deep Learning for RNA Secondary Structure Determination: Gauging Generalizability and Broadening the Scope of Traditional Methods is a research paper published in bioRxiv (Cold Spring Harbor Laboratory) (2025). On theSindex it has a DataRank of 0.

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Enrichment:Funding (3 grants)OA: greenTopics (18)IDs (PubMed, OpenAlex)

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Authors (6)

Ting-Yuan Wang ORCID,Ryan Krueger ORCID,David H. Mathews ORCID,Max Ward ORCID,Sharon Aviran ORCID

Live enrichment

PubMed:41278894 OpenAlex:https://openalex.org/W4415989846

National Institutes of Health

Grant: R21GM148835

National Institutes of Health

Grant: R35GM145283

U.S. National Science Foundation

Grant: UWSC13223

greenLicense: cc-by

repositoryhttps://www.biorxiv.org/content/biorxiv/early/2025/11/07/2025.11.04.686644.full.pdf

GREENhttps://www.biorxiv.org/content/biorxiv/early/2025/11/07/2025.11.04.686644.full.pdf

repositoryhttps://www.biorxiv.org/content/biorxiv/early/2025/11/07/2025.11.04.686644.full.pdf

publisherhttps://syndication.highwire.org/content/doi/10.1101/2025.11.04.686644

repositoryhttps://doi.org/10.1101/2025.11.04.686644

Fields of Study

RNA and protein synthesis mechanismsProtein Structure and DynamicsRNA modifications and cancerBiologyMedicineComputer Science

Keywords

Deep learningGeneralizability theoryGeneralizationScalabilityNucleic acid structureDomain (mathematical analysis)Benchmark (surveying)Scope (computer science)RNA foldingRNA structure predictionSHAPEStructure probing