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Systematic measurement of combination drug landscapes to predict in vivo treatment outcomes for tuberculosis

bioRxiv (Cold Spring Harbor Laboratory)(2021)10.1101/2021.02.03.429579Source: DataRank Database

Systematic measurement of combination drug landscapes to predict in vivo treatment outcomes for tuberculosis is a research paper published in bioRxiv (Cold Spring Harbor Laboratory) (2021). On theSindex it has a DataRank of 0. It has been cited 5 times.

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Open Access5 citations

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›Methodology & internals

datarank_citation_only_1hop_v6· scope data_onlyMethodology

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Enrichment:Funding (1 grants)OA: greenTopics (28)IDs (PubMed, OpenAlex)SDGs (2)

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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 (7)

Talia Greenstein ORCID,Nhi Van ORCID,Yonatan N. Degefu ORCID,Michaela C. Olson ORCID,Artem Sokolov ORCID

Live enrichment

PubMed:26207759 OpenAlex:https://openalex.org/W3128927476

National Institutes of Health

Grant: 5U54CA225088-05

Systems Pharmacology of Therapeutic and Adverse Responses to ImmuneCheckpoint and Small Molecule Drugs

greenLicense: cc-by-nc-nd

repositoryhttps://www.biorxiv.org/content/biorxiv/early/2021/02/06/2021.02.03.429579.full.pdf

publisherhttps://syndication.highwire.org/content/doi/10.1101/2021.02.03.429579

repositoryhttps://doi.org/10.1101/2021.02.03.429579

https://doi.org/10.1016/j.cels.2021.08.004

Fields of Study

Tuberculosis Research and EpidemiologyCancer therapeutics and mechanismsPhenothiazines and Benzothiazines Synthesis and Activities0301 basic medicine03 medical and health sciences

Keywords

TuberculosisDrugCombination therapyMachine learningIn vivoMedicineDrug discoveryArtificial intelligenceMycobacterium tuberculosisDrug repositioningComputational biologyComputer sciencePharmacologyBioinformaticsBiologyPathologyBiotechnologyDrug CombinationsMiceTreatment OutcomeAntitubercular AgentsAnimalsArticle

Sustainable Development Goals

SDG 3: 3. Good healthSDG 0: Good health and well-being