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Machine learning methods for endocrine disrupting potential identification based on single-cell data

Chemical Engineering Science(2023)10.1016/j.ces.2023.119086Source: DataRank Database

Machine learning methods for endocrine disrupting potential identification based on single-cell data is a research paper published in Chemical Engineering Science (2023). On theSindex it has a DataRank of 0. It has been cited 9 times.

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0DataRank · unranked

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Open Access9 citations

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›Methodology & internals

datarank_citation_only_1hop_v6· scope data_onlyMethodology

Pipeline:MetadataData-paper checkEnrichmentCitation networkScoring

Enrichment:Funding (11 grants)Impact (FWCI 1.28)OA: greenTopics (18)IDs (PubMed, PMC, OpenAlex)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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Authors (9)

Adam T. Szafran ORCID,Anh Tran ORCID,Hari S. Ganesh ORCID,Fabio Stossi ORCID,Lan Zhou ORCID

Live enrichment

PubMed:37637227 PMC:PMC10448728 OpenAlex:https://openalex.org/W4384701053

National Institutes of Health

Grant: CA125123

National Institutes of Health

Grant: DK56338

National Institutes of Health

Grant: ES030285

National Institutes of Health

Grant: P42-ES027704

Cancer Prevention and Research Institute of Texas

Grant: RP150578

Cancer Prevention and Research Institute of Texas

Grant: RP170719

NIDDK NIH HHS

Grant: P30 DK056338

NIEHS NIH HHS

Grant: P42 ES027704

NIEHS NIH HHS

Grant: P30 ES030285

NCI NIH HHS

Grant: P30 CA125123

University of Connecticut

FWCI

1.28

Citation Percentile

0.8%

Citation Trend

2023

2024

2025

2026

greenLicense: http://www.elsevier.com/open-access/userlicense/1.0/

repositoryhttps://pmc.ncbi.nlm.nih.gov/articles/PMC10448728/pdf/nihms-1922948.pdf

publisherhttps://api.elsevier.com/content/article/PII:S0009250923006425?httpAccept=text/xml

publisherhttps://api.elsevier.com/content/article/PII:S0009250923006425?httpAccept=text/plain

repositoryhttps://www.ncbi.nlm.nih.gov/pmc/articles/10448728

Fields of Study

Computational Drug Discovery MethodsCell Image Analysis TechniquesAdvanced Biosensing Techniques and Applications

Keywords

Support vector machineComputer sciencePreprocessorMachine learningArtificial intelligenceData pre-processingIdentification (biology)Principal component analysisData miningBiologyPredictive modelingendocrine disrupting chemicalsclassification analysisHigh Throughput MicroscopyEstrogen Receptor Activity

Sustainable Development Goals

SDG 0: Life in Land

Machine learning methods for endocrine disrupting potential identification based on single-cell data

FAIR Checklist

Authors (9)

Funding (11 grants)

Impact

Open Access

Topics

Annotations (Europe PMC)