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HydRA: Deep-learning models for predicting RNA-binding capacity from protein interaction association context and protein sequence

Molecular Cell(2023)10.1016/j.molcel.2023.06.019Source: DataRank Database

HydRA: Deep-learning models for predicting RNA-binding capacity from protein interaction association context and protein sequence is a research paper published in Molecular Cell (2023). On theSindex it has a DataRank of 0. It has been cited 51 times.

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Open Access51 citations

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Enrichment:Funding (8 grants)OA: hybridTopics (11)IDs (PubMed, PMC)Annotations (2)

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Accessible (1/2)

Open Access

Interoperable (0/2)

Reusable (0/3)

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Authors (13)

Kristopher W. Brannan ORCID,Katannya Kapeli,Samuel S. Park ORCID,Hui Tan,Maya L. Gosztyla ORCID

Live enrichment

PubMed:37421941 PMC:PMC11098078

National Institutes of Health

Grant: R01 HG004659

National Institutes of Health

Grant: U24 HG009889

National Institute of Neurological Disorders and Stroke

Grant: K22 K22NS112678

Cancer Prevention and Research Institute of Texas

Grant: RR220017

NINDS NIH HHS

Grant: K22 NS112678

The Paul G Allen Frontiers Group

University of California, San Diego

National University of Singapore

hybridLicense: cc-by

publisherhttps://doi.org/10.1016/j.molcel.2023.06.019

publisherhttps://api.elsevier.com/content/article/PII:S1097276523004665?httpAccept=text/xml

publisherhttps://api.elsevier.com/content/article/PII:S1097276523004665?httpAccept=text/plain

repositoryhttps://pmc.ncbi.nlm.nih.gov/articles/PMC11098078/pdf/nihms-1915946.pdf

MeSH Terms

AnimalsHumansHydraRNABinding SitesProtein BindingDeep Learning

Keywords

RNA-binding proteinsprotein-protein interaction networkMachine LearningRna-binding Domains

HydRA: Deep-learning models for predicting RNA-binding capacity from protein interaction association context and protein sequence

FAIR Checklist

Authors (13)

Funding (8 grants)

Open Access

Topics

Annotations (Europe PMC)