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

Advances in machine learning for ABCA4-related retinopathy: segmentation and phenotyping

International Ophthalmology(2025)10.1007/s10792-025-03690-4Source: DataRank Database

Advances in machine learning for ABCA4-related retinopathy: segmentation and phenotyping is a research paper published in International Ophthalmology (2025). On theSindex it has a DataRank of 0. It has been cited 2 times.

N/A

0DataRank · unranked

Scored on demand from live citation data

Open Access2 citations

Download PDF

Cite:

Linked data & code

omim

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 (5 grants)Impact (FWCI 0.76)OA: hybridTopics (37)IDs (PubMed, PMC, OpenAlex)Annotations (1)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 (5)

Brian P. Brooks ORCID,Brett G. Jeffrey ORCID,Benjamin D. Solomon ORCID,Melissa A Haendel ORCID,Yousif J. Shwetar ORCID

Live enrichment

PubMed:40699379 PMC:PMC12287237 OpenAlex:https://openalex.org/W4412584150

National Human Genome Research Institute

Grant: 1RM1HG010860

National Human Genome Research Institute

Grant: Intramural Research Program

NHGRI NIH HHS

Grant: RM1 HG010860

National Institutes of Health

Grant: 5RM1HG010860-03

A phenomics-first resource for interpretation of variants

University of North Carolina at Chapel Hill

FWCI

0.76

Citation Percentile

0.7%

Citation Trend

2025

2026

hybridLicense: cc-by

journalhttps://link.springer.com/content/pdf/10.1007/s10792-025-03690-4.pdf

publisherhttps://link.springer.com/content/pdf/10.1007/s10792-025-03690-4.pdf

publisherhttps://link.springer.com/article/10.1007/s10792-025-03690-4

journalhttps://doi.org/10.1007/s10792-025-03690-4

repositoryhttps://pubmed.ncbi.nlm.nih.gov/40699379

Fields of Study

Retinal Development and DisordersRetinopathy of Prematurity StudiesRetinal Diseases and Treatments0202 electrical engineering, electronic engineering, information engineering02 engineering and technology

MeSH Terms

Machine LearningStargardt DiseaseElectroretinographyHumansPhenotypeRetinaRetinal DiseasesVisual AcuityATP-Binding Cassette TransportersTomography, Optical Coherence

Keywords

Stargardt diseaseSegmentationArtificial intelligenceMedicineMicroperimetryABCA4Machine learningComputer scienceOphthalmologyRetinalPhenotypingRetinal Image SegmentationAbca4-related RetinopathyPhenotypeRetinal DiseasesElectroretinographyVisual AcuityHumansATP-Binding Cassette TransportersReviewTomography, Optical CoherenceRetina

Sustainable Development Goals

SDG 0: Industry, innovation and infrastructure

Advances in machine learning for ABCA4-related retinopathy: segmentation and phenotyping

FAIR Checklist

Authors (5)

Funding (5 grants)

Impact

Open Access

Topics

Annotations (Europe PMC)