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Using Bibliometric Analysis and Machine Learning to Identify Compounds Binding to Sialidase-1

ACS Omega(2021)10.1021/acsomega.0c05591Source: DataRank Database

Using Bibliometric Analysis and Machine Learning to Identify Compounds Binding to Sialidase-1 is a research paper published in ACS Omega (2021). On theSindex it has a DataRank of 0.416. It has been cited 15 times.

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0.416DataRank · unranked

Scored on demand from live citation data

Open Access15 citations

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datarank_citation_only_1hop_v6· scope data_onlyMethodology

Pipeline:MetadataData-paper checkEnrichmentCitation networkScoring

Enrichment:Funding (9 grants)OA: goldTopics (32)IDs (PubMed, PMC)Annotations (1)SDGs (1)

FAIR Checklist

Context only (not used in score)

Findable (1/2)

Has DOI

Accessible (1/2)

Open Access

Interoperable (0/2)

Reusable (0/3)

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DataRank Breakdown

Base Score 100%Citation Network 0%

Base Score Contribution

0.416

From this paper's citation signal

Citation Network Contribution

Citation network not refreshed for this result

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Authors (7)

Nancy Baker ORCID,Daniel H. Foil ORCID,Kimberley M. Zorn,Fabio Urbina,Ana C. Puhl ORCID

Live enrichment

PubMed:33553934 PMC:PMC7860073

National Institute of Neurological Disorders and Stroke

Grant: 1R43NS107079-01

Enzyme Replacement Therapy For Infantile Onset Neuronal Ceroid Lipofuscinoses

National Institute of Neurological Disorders and Stroke

Grant: 3R43NS107079-01S1

NIH Office of the Director

Grant: DP7OD020317

National Institute of General Medical Sciences

Grant: R44GM122196-02A1

NIGMS NIH HHS

Grant: R44 GM122196

NINDS NIH HHS

Grant: R43 NS107079

NCATS NIH HHS

Grant: R44 TR000942

National Institutes of Health

Grant: 2R44GM122196-04A1

Centralized assay datasets for modelling support of small drug discovery organizations

National Institutes of Health

Grant: 5DP7OD020317-04

UNC ImPACT Grant (Immersion Program to Advance Career Training)

goldLicense: http://pubs.acs.org/page/policy/authorchoice_ccbyncnd_termsofuse.html

publisherhttp://pubs.acs.org/doi/pdf/10.1021/acsomega.0c05591

publisherhttps://pubs.acs.org/doi/pdf/10.1021/acsomega.0c05591

Europe_PMChttps://europepmc.org/articles/PMC7860073

Europe_PMChttps://europepmc.org/articles/PMC7860073?pdf=render

https://doi.org/10.1021/acsomega.0c05591

Fields of Study

0301 basic medicine03 medical and health sciences0303 health sciences

Keywords

glycoproteinultrarare lysosomal diseaseIdentify Compounds BindingChemical Sciences not elsewhere classifiedInformation Systems not elsewhere classifiedbioactivity dataBiochemistryLSDGeneticsdrug candidatesscore compound librariesQD1-999curatedPharmacologyNEU 1 gene resultmoleculebibliometricmodellysosomal enzyme sialidase -1approachtestingultrarare lysosomal storage disorderChemistrydisease drug discoverydiseases impact hundredslysosomal storage diseasesialic acid moietiesproteinBiological Sciences not elsewhere classified

Sustainable Development Goals

SDG 3: 3. Good health

Using Bibliometric Analysis and Machine Learning to Identify Compounds Binding to Sialidase-1

FAIR Checklist

DataRank Breakdown

Authors (7)

Funding (9 grants)

Open Access

Topics

Annotations (Europe PMC)