Pilot corpus: NIH-funded biomedical datasets. Any DOI can be scored on demand — network effects sharpen as coverage grows.

Interpretable Fine‐Grained Phenotypes of Subcellular Dynamics via Unsupervised Deep Learning

Advanced Science(2024)10.1002/advs.202403547Source: DataRank Database

Interpretable Fine‐Grained Phenotypes of Subcellular Dynamics via Unsupervised Deep Learning is a research paper published in Advanced Science (2024). On theSindex it has a DataRank of 0. It has been cited 4 times.

N/A

0DataRank · unranked

Scored on demand from live citation data

Open Access4 citations

Download PDF

Cite:

DataRank reads this dataset's downstream impact straight off the citation graph — no black box, no proprietary weighting. How is this computed?

›Methodology & internals

datarank_citation_only_1hop_v6· scope data_onlyMethodology

Pipeline:MetadataData-paper checkEnrichmentCitation networkScoring

Enrichment:Funding (6 grants)Impact (FWCI 1.36)OA: goldTopics (41)IDs (PubMed, PMC, OpenAlex)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)

FAIR checklist signals are shown for context only and do not affect DataRank scoring.

Run a calibrated FAIR evaluation for this paper →

We only score data papers we can read in full — never from an abstract alone.

Authors (4)

Hee June Choi,Lucy Woodbury ORCID,Kwonmoo Lee ORCID,Chuangqi Wang ORCID

Live enrichment

PubMed:39239705 PMC:PMC11538677 OpenAlex:https://openalex.org/W4402317208

National Heart, Lung, and Blood Institute

Grant: R01HL163513

National Institute of General Medical Sciences

Grant: R35GM133725

National Institute of General Medical Sciences

Grant: R15GM122012

National Institutes of Health

Grant: 1R35GM133725-01

Unraveling subcellular heterogeneity of molecular coordination by machine learning

National Institutes of Health

Grant: 1R15GM122012-01

Spatiotemporal Coordination of Actin Regulators and Traction Forces in Heterogeneous Cell Protrusions

National Institutes of Health

Grant: 5R01HL163513-02

High-throughput Phenotyping of iPSC-derived Airway Epithelium by Multiscale Machine Learning Microscopy

FWCI

1.36

Citation Percentile

0.8%

Citation Trend

2023

2025

goldLicense: cc-by

journalhttps://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/advs.202403547

publisherhttps://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/advs.202403547

publisherhttps://advanced.onlinelibrary.wiley.com/doi/pdf/10.1002/advs.202403547

journalhttps://doi.org/10.1002/advs.202403547

repositoryhttps://pubmed.ncbi.nlm.nih.gov/39239705

Fields of Study

Cell Image Analysis TechniquesComputational Drug Discovery MethodsGene Regulatory Network Analysis02 engineering and technology0202 electrical engineering, electronic engineering, information engineeringDeep LearningUnsupervised Machine LearningPhenotypeHumansCell MovementNeural Networks, Computer

MeSH Terms

Unsupervised Machine LearningDeep LearningCell MovementHumansPhenotypeNeural Networks, Computer

Keywords

AutoencoderComputer scienceArtificial intelligenceUnsupervised learningDeep learningMachine learningDynamics (music)Feature (linguistics)Deep neural networksFeature learningArtificial neural networkPattern recognition (psychology)Phenotypingcell migrationLive Cell ImagingMorphodynamicsScienceQPhenotypeCell MovementHumansNeural Networks, ComputerResearch ArticleUnsupervised Machine Learning

Sustainable Development Goals

SDG 0: Reduced inequalities