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

Nucleotide context models outperform protein language models for predicting antibody affinity maturation

PLoS Computational Biology(2025)10.1371/journal.pcbi.1013758Source: DataRank Database

Nucleotide context models outperform protein language models for predicting antibody affinity maturation is a research paper published in PLoS Computational Biology (2025). On theSindex it has a DataRank of 0. It has been cited 1 time.

N/A

0DataRank · unranked

Scored on demand from live citation data

Open Access1 citations

Download PDF

Cite:

Linked data & code

doi

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 (8 grants)Impact (FWCI 0.41)OA: goldTopics (33)IDs (PubMed, PMC, OpenAlex)Annotations (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 (9)

Kevin Sung ORCID,Hugh K. Haddox ORCID,A. B. Vora ORCID,Tatsuya Araki ORCID,Gabriel D. Victora ORCID

Live enrichment

PubMed:41325480 PMC:PMC12685221 OpenAlex:https://openalex.org/W4416872945

National Institutes of Health

Grant: R01-AI146028

National Institutes of Health

Grant: R56-HG013117

National Institutes of Health

Grant: R01-HG013117

National Institutes of Health

Grant: S10OD028685

NIAID NIH HHS

Grant: R01 AI146028

NHGRI NIH HHS

Grant: R01 HG013117

NHGRI NIH HHS

Grant: R56 HG013117

Howard Hughes Medical Institute

FWCI

0.41

Citation Percentile

0.7%

Citation Trend

2025

goldLicense: cc-by

journalhttps://proceedings.neurips.cc/paper_files/paper/2023/file/cac723e5ff29f65e3fcbb0739ae91bee-Paper-Datasets_and_Benchmarks.pdf

publisherhttps://proceedings.neurips.cc/paper_files/paper/2023/file/cac723e5ff29f65e3fcbb0739ae91bee-Paper-Datasets_and_Benchmarks.pdf

publisherhttps://dx.plos.org/10.1371/journal.pcbi.1013758

journalhttps://doi.org/10.1371/journal.pcbi.1013758

repositoryhttps://pubmed.ncbi.nlm.nih.gov/41325480

Fields of Study

vaccines and immunoinformatics approachesMonoclonal and Polyclonal Antibodies ResearchT-cell and B-cell ImmunologyAnimalsHumansMiceReceptors, Antigen, B-CellAntibody AffinityComputational BiologyB-LymphocytesEvolution, MolecularSomatic Hypermutation, ImmunoglobulinNucleotidesPhylogeny

MeSH Terms

AnimalsAntibody AffinityB-LymphocytesHumansNucleotidesPhylogenyReceptors, Antigen, B-CellEvolution, MolecularComputational BiologySomatic Hypermutation, ImmunoglobulinMice

Keywords

Affinity maturationContext (archaeology)Somatic hypermutationRepertoireLanguage modelConvolutional neural networkComputational modelFeature selection

Nucleotide context models outperform protein language models for predicting antibody affinity maturation

FAIR Checklist

Authors (9)

Funding (8 grants)

Impact

Open Access

Topics

Annotations (Europe PMC)