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A knowledge graph representation learning approach to predict novel kinase–substrate interactions

Molecular Omics(2022)10.1039/d1mo00521aSource: DataRank Database

A knowledge graph representation learning approach to predict novel kinase–substrate interactions is a research paper published in Molecular Omics (2022). On theSindex it has a DataRank of 0. It has been cited 10 times.

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Open Access10 citations

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datarank_citation_only_1hop_v6· scope data_onlyMethodology

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Enrichment:Funding (3 grants)OA: hybridTopics (27)IDs (PubMed, PMC)Annotations (2)

FAIR Checklist

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Findable (1/2)

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Accessible (1/2)

Open Access

Interoperable (0/2)

Reusable (0/3)

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Authors (5)

Karen Ross ORCID,Chuming Chen ORCID,Julie Cowart ORCID,Cathy Wu ORCID,Sachin Gavali ORCID

Live enrichment

PubMed:35975455 PMC:PMC9621340

National Institutes of Health

Grant: R35GM141873

National Academy of Sciences

Grant: G20201001105037737

National Institutes of Health

Grant: 5R35GM141873-04

Protein Knowledge Networks and Semantic Computing for Disease Discovery

hybridLicense: cc-by-nc

publisherhttps://pubs.rsc.org/en/content/articlepdf/2022/mo/d1mo00521a

publisherhttps://academic.oup.com/molecular-omics/article-pdf/18/9/853/65030794/d1mo00521a.pdf

repositoryhttps://www.ncbi.nlm.nih.gov/pmc/articles/9621340

https://doi.org/10.1039/d1mo00521a

https://dx.doi.org/10.48550/arxiv.2206.02290

Fields of Study

0301 basic medicine0303 health sciences03 medical and health sciencesHumansGene OntologyPattern Recognition, AutomatedProteomeSubstrate Specificity

MeSH Terms

HumansProteomeSubstrate SpecificityPattern Recognition, AutomatedGene Ontology

Keywords

FOS: Computer and information sciencesProteomeComputer Science - Artificial IntelligenceMolecular Networks (q-bio.MN)Quantitative Biology - Quantitative MethodsPattern Recognition, AutomatedSubstrate SpecificityChemistryGene OntologyArtificial Intelligence (cs.AI)FOS: Biological sciencesHumansQuantitative Biology - Molecular NetworksQuantitative Methods (q-bio.QM)