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Knowledge-guided deep learning models of drug toxicity improve interpretation

bioRxiv (Cold Spring Harbor Laboratory)(2022)10.1101/2022.02.28.482300Source: DataRank Database

Knowledge-guided deep learning models of drug toxicity improve interpretation is a research paper published in bioRxiv (Cold Spring Harbor Laboratory) (2022). On theSindex it has a DataRank of 0. It has been cited 2 times.

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Open Access2 citations

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datarank_citation_only_1hop_v6· scope data_onlyMethodology

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Enrichment:Funding (4 grants)OA: greenTopics (26)IDs (PubMed, OpenAlex)SDGs (1)

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Authors (3)

Joseph D. Romano ORCID,Jason H. Moore ORCID,Yun Hao ORCID

Live enrichment

PubMed:19162232 OpenAlex:https://openalex.org/W4214704330

National Institutes of Health

Grant: 5P30ES013508-04

CORE--Endocrine and Reproduction Disruption

National Institutes of Health

Grant: 1K99LM013646-01A1

Discovering clinical endpoints of toxicity via graph machine learning and semantic data analysis

National Institutes of Health

Grant: 5R01LM010098-14

Bioinformatics Strategies for Genome Wide Association Studies

National Institutes of Health

Grant: 1R01AG066833-01A1

Artificial Intelligence Strategies for Alzheimer's Disease Research

greenLicense: cc-by-nc-nd

repositoryhttps://www.biorxiv.org/content/biorxiv/early/2022/03/09/2022.02.28.482300.full.pdf

publisherhttps://syndication.highwire.org/content/doi/10.1101/2022.02.28.482300

repositoryhttps://doi.org/10.1101/2022.02.28.482300

repositoryhttps://repository.upenn.edu/handle/20.500.14332/59701

Fields of Study

Computational Drug Discovery MethodsCell Image Analysis TechniquesExplainable Artificial Intelligence (XAI)0301 basic medicine0303 health sciences03 medical and health sciences

Keywords

InterpretabilityToxicityIn silicoComputational biologyCytotoxicityVirtual screeningMechanism (biology)DrugDrug discoveryBiologyPharmacologyBioinformaticsComputer scienceArtificial intelligenceMedicineGeneticsGeneIn vitroInternal medicineArticle

Sustainable Development Goals

SDG 0: Good health and well-being