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DeepPurpose: a deep learning library for drug–target interaction prediction

Bioinformatics(2020)10.1093/bioinformatics/btaa1005Source: DataRank Database

DeepPurpose: a deep learning library for drug–target interaction prediction is a research paper published in Bioinformatics (2020). On theSindex it has a DataRank of 0. It has been cited 484 times.

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0DataRank · unranked

Scored on demand from live citation data

Open Access484 citations

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›Methodology & internals

datarank_citation_only_1hop_v6· scope data_onlyMethodology

Pipeline:MetadataData-paper checkEnrichmentCitation networkScoring

Enrichment:Funding (19 grants)OA: hybridTopics (22)IDs (PubMed, PMC)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 (6)

Tianfan Fu ORCID,Lucas M. Glass ORCID,Marinka Zitnik ORCID,Cao Xiao ORCID,Jimeng Sun ORCID

Live enrichment

PubMed:33275143 PMC:PMC8016467

NSF

Grant: IIS-2030459

NSF

Grant: IIS-2033384

NSF

Grant: SCH-2014438

NSF

Grant: IIS-1418511

NSF

Grant: CCF-1533768

NSF

Grant: IIS-1838042

NIH

Grant: R01 1R01NS107291-01

NIH

Grant: R56HL138415

NINDS NIH HHS

Grant: R01 NS107291

National Institutes of Health

Grant: 1R01NS107291-01

Big Data and Deep Learning for the Interictal-Ictal-Injury Continuum

National Science Foundation

Grant: 1533768

XPS: FULL: DSD: A Parallel Tensor Infrastructure (ParTI!) for Data Analysis

National Science Foundation

Grant: 1418511

SCH: INT: Collaborative Research: High-throughput Phenotyping on Electronic Health Records using Multi-Tensor Factorization

National Science Foundation

Grant: 2030459

RAPID:Collaborative Research: Computational Drug Repurposing for COVID-19

National Science Foundation

Grant: 2033384

Workshop on Drug Repurposing for Future Pandemics

National Science Foundation

Grant: 1838042

BigData:IA:Collaborative Research: TIMES: A tensor factorization platform for spatio-temporal data

National Institutes of Health

Grant: 1R56HL138415-01

Interpretable Deep Learning Model for Longitudinal Electronic Health Records and Applications to Heart Failure Prediction

National Science Foundation

Grant: 2014438

SCH:INT: Collaborative Research: Deep Sense: Interpretable Deep Learning for Zero-effort Phenotype Sensing and Its Application to Sleep Medicine

NIH

Harvard Data Science Initiative

hybridLicense: cc-by

publisherhttps://academic.oup.com/bioinformatics/article-pdf/36/22-23/5545/36855903/btaa1005.pdf

publisherhttp://academic.oup.com/bioinformatics/advance-article-pdf/doi/10.1093/bioinformatics/btaa1005/34883308/btaa1005.pdf

publisherhttps://academic.oup.com/bioinformatics/article-pdf/36/22-23/5545/50716143/btaa1005.pdf

repositoryhttps://arxiv.org/pdf/2004.08919

repositoryhttps://www.ncbi.nlm.nih.gov/pmc/articles/8016467

Fields of Study

0301 basic medicine0303 health sciences03 medical and health sciences

MeSH Terms

ProteinsPharmaceutical PreparationsDrug DiscoveryDrug DevelopmentDeep Learning

Keywords

FOS: Computer and information sciencesComputer Science - Machine LearningProteinsMachine Learning (stat.ML)Quantitative Biology - Quantitative MethodsApplications NotesMachine Learning (cs.LG)Deep LearningDrug DevelopmentPharmaceutical PreparationsStatistics - Machine LearningFOS: Biological sciencesDrug DiscoveryQuantitative Methods (q-bio.QM)

Sustainable Development Goals

SDG 3: 3. Good health