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Fully interpretable deep learning model of transcriptional control

Bioinformatics(2020)10.1093/bioinformatics/btaa506Source: DataRank Database

Fully interpretable deep learning model of transcriptional control is a research paper published in Bioinformatics (2020). On theSindex it has a DataRank of 0. It has been cited 36 times.

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0DataRank · unranked

Scored on demand from live citation data

Open Access36 citations

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›Methodology & internals

datarank_citation_only_1hop_v6· scope data_onlyMethodology

Pipeline:MetadataData-paper checkEnrichmentCitation networkScoring

Enrichment:Funding (3 grants)OA: goldTopics (19)IDs (PubMed)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 (3)

Kenneth Barr ORCID,John Reinitz ORCID,Yi Liu ORCID

Live enrichment

PubMed:32657418

National Institutes of Health

Grant: 2R01OD010936-27A1

Physiological Model of Gene Regulation in Drosophila

National Institutes of Health

Grant: 5R01NS109361-03

Longitudinal multimodal mapping to decipher the neurovascular impact of microinfarcts

NIH HHS

Grant: R01 OD010936

goldLicense: CC BY NC

https://doi.org/10.1101/655639

https://academic.oup.com/bioinformatics/article-pdf/36/Supplement_1/i499/33489229/btaa506.pdf

https://doi.org/10.1093/bioinformatics/btaa506

https://pubmed.ncbi.nlm.nih.gov/32657418

http://dx.doi.org/10.1093/bioinformatics/btaa506

Fields of Study

0301 basic medicine0303 health sciences03 medical and health sciencesDeep LearningGene Expression RegulationGenomicsMachine LearningNeural Networks, Computer

MeSH Terms

Deep LearningGene Expression RegulationGenomicsMachine LearningNeural Networks, Computer

Keywords

Machine LearningDeep LearningGene Expression RegulationSystems Biology and NetworksGenomicsNeural Networks, Computer

Sustainable Development Goals

SDG 3: 3. Good health