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Discovery of antimicrobial peptides in the global microbiome with machine learning

Cell(2024)10.1016/j.cell.2024.05.013Source: DataRank Database

Discovery of antimicrobial peptides in the global microbiome with machine learning is a research paper published in Cell (2024). On theSindex it has a DataRank of 0. It has been cited 353 times.

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0DataRank · unranked

Scored on demand from live citation data

Open Access353 citations

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datarank_citation_only_1hop_v6· scope data_onlyMethodology

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Enrichment:Funding (25 grants)OA: hybridTopics (32)IDs (PubMed, PMC)Annotations (2)SDGs (1)

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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 (17)

Marcelo D. T. Torres ORCID,Yiqian Duan ORCID,Álvaro Rodríguez del Río ORCID,Thomas Schmidt ORCID,Hui Chong ORCID

Live enrichment

PubMed:38843834 PMC:PMC11666328

National Natural Science Foundation of China

Grant: T2225015

National Natural Science Foundation of China

Grant: 61932008

Horizon 2020 Marie Skłodowska-Curie Actions

Grant: 713673

Innovative doctoral programme for talented early-stage researchers in Spanish host organisations excellent in the areas of Science, Technology, Engineering and Mathematics (STEM).

Science and Technology Commission of Shanghai Municipality

Grant: 23JS1410100

Science and Technology Commission of Shanghai Municipality

Grant: 22JC1410900

Science and Technology Commission of Shanghai Municipality

Grant: 2018SHZDZX01

Australian Research Council

Grant: FT230100724

ARC Future Fellowships - Grant ID: FT230100724

Fundación La Caixa

Grant: LCF/BQ/DI18/11660009

National Key Research and Development Program of China

Grant: 2020YFA0712403

National Key Research and Development Program of China

Grant: 2023YFF1204800

NIGMS NIH HHS

Grant: R35 GM138201

National Institutes of Health

Grant: 1R35GM138201-01

Combining chemical and computational tools for predictive models of microbiome communities

European Molecular Biology Laboratory

Defense Threat Reduction Agency

National Key Laboratory of Human Factors Engineering

American Institute of Chemical Engineers

Brain and Behavior Research Foundation

National Institutes of Health

Procter and Gamble

United Therapeutics Corporation

ACE Foundation

Perelman School of Medicine, University of Pennsylvania

FEDER

Horizon 2020 Framework Programme

University of Pennsylvania

hybridLicense: cc-by

publisherhttp://www.cell.com/article/S0092867424005221/pdf

publisherhttps://api.elsevier.com/content/article/PII:S0092867424005221?httpAccept=text/xml

publisherhttps://api.elsevier.com/content/article/PII:S0092867424005221?httpAccept=text/plain

repositoryhttps://digital.csic.es/bitstream/10261/389013/1/discovery_antimicrobial_Santos.pdf

repositoryhttps://edoc.mdc-berlin.de/id/eprint/24442/1/24442oa.pdf

Fields of Study

0301 basic medicine03 medical and health sciences0303 health sciences

MeSH Terms

AnimalsHumansMiceBacteriaAnti-Bacterial AgentsMetagenomeMicrobiotaMachine LearningGastrointestinal MicrobiomeAntimicrobial Peptides

Keywords

Antimicrobial activityAntibiotic ResistanceAntimicrobial peptidesMachine LearningMetagenomicsAntibiotic DiscoveryGlobal Microbiome570610MiceHumansAnimalsBacteriaMicrobiotaAnti-Bacterial AgentsGastrointestinal MicrobiomeCardiovascular and Metabolic DiseasesMetagenomeTopic 2: Molecular Processes and Therapies

Sustainable Development Goals

SDG 3: 3. Good health