Demo corpus. Scores are computed on a select set of biomedical paper/datasets and may be inaccurate for papers outside this corpus — DataRank relies on network effects that improve with scale. We aim to expand this into a fully open resource pending additional funding.

Precision Medicine for Adolescent and Young Adult (AYA) Oncology

Journal of Personalized Medicine(2023)10.3390/jpm13101449Source: DataRank Database

Precision Medicine for Adolescent and Young Adult (AYA) Oncology is a research paper published in Journal of Personalized Medicine (2023). On theSindex it has a DataRank of 0.231. It has been cited 1 time, with 1 citing works in its 1-hop citation network.

N/A

0.231DataRank · unranked

0.231

1 citations · base score 0.693

Cite:

datarank_citation_only_1hop_v6· scope data_onlyMethodology

Abstract

Precision medicine, also referred to as “personalized medicine” is an approach in customizing disease prevention and treatment by integrating the unique differences found in individuals, typically identified through molecular or genomic characterization [...]

›Data sources & pipeline

Pipeline:MetadataData-paper checkEnrichmentCitation networkScoring

Enrichment:Pending

FAIR Checklist

Context only (not used in score)

Findable (1/2)

Has DOI

Accessible (0/2)

Interoperable (0/2)

Reusable (0/3)

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

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DataRank Breakdown

Base Score 45%Citation Network 55%

Base Score Contribution

0.104

From this paper's citation signal

Citation Network Contribution

0.127

From 1 citing papers with measurable signal

Learn more about DataRank methodology →

Why this DataRank?

DataRank blends this paper's own citation count with the influence of the papers that cite it. Here, roughly 45% comes from its base citations and 55% from the citation network (1 citing paper contributed measurable signal).

Base score B(p): log1p(citation_count) — grows sub-linearly, so a paper with 1,000 citations is not 10× a paper with 100.
Network N(p): Σ over citers of log1p(C_q) ÷ max(outdegree_q, 1). Being cited by a highly-cited paper with few references counts most.
Damping factor d = 0.85: DataRank = (1−d)·B(p) + d·N(p) — the two cards above are each already multiplied by their share.
Self-citations excluded: Citers sharing any OpenAlex author ID with this paper are filtered out before the network sum.

Citers are pulled from OpenAlex sorted by cited_by_count:descand capped per paper, so when the cap binds we keep the highest-signal references and the score is reproducible across reruns.

Read the full methodology →

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Node colors:CenterData PaperData + Open AccessNon-dataSelected & links| Node size = percentile rank