🏆 Finalist — NIH Data Sharing Index (“S-Index”) Challenge
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.

RORα, a Potential Tumor Suppressor and Therapeutic Target of Breast Cancer

International Journal of Molecular Sciences(2012)10.3390/ijms131215755Source: DataRank Database

RORα, a Potential Tumor Suppressor and Therapeutic Target of Breast Cancer is a research paper published in International Journal of Molecular Sciences (2012). On theSindex it has a DataRank of 3.6. It has been cited 102 times, with 75 citing works in its 1-hop citation network.

N/A
3.6DataRank · unranked
3.6
Open Access102 citations · base score 4.6
Cite:
datarank_citation_only_1hop_v6· scope data_onlyMethodology

Abstract

The function of the nuclear receptor (NR) in breast cancer progression has been investigated for decades. The majority of the nuclear receptors have well characterized natural ligands, but a few of them are orphan receptors for which no ligand has been identified. RORα, one member of the retinoid orphan nuclear receptor (ROR) subfamily of orphan receptors, regulates various cellular and pathological activities. RORα is commonly down-regulated and/or hypoactivated in breast cancer compared to normal mammary tissue. Expression of RORα suppresses malignant phenotypes in breast cancer cells, in vitro and in vivo. Activity of RORα can be categorized into the canonical and non-canonical nuclear receptor pathways, which in turn regulate various breast cancer cellular function, including cell proliferation, apoptosis and invasion. This information suggests that RORα is a potent tumor suppressor and a potential therapeutic target for breast cancer.

Data sources & pipeline
Pipeline:MetadataData-paper checkEnrichmentCitation networkScoring
Enrichment:Pending

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.

      DataRank Breakdown

      Base Score 19%Citation Network 81%

      Base Score Contribution

      0.695

      From this paper's citation signal

      Citation Network Contribution

      2.9

      From 71 citing papers with measurable signal

      Learn more about DataRank methodology →

      Top 5 citers driving the network score

      Ranked by citation count — the same ordering the engine uses when summing log1p(Cq) over citers.

      Why this DataRank?

      DataRank blends this paper's own citation count with the influence of the papers that cite it. Here, roughly 19% comes from its base citations and 81% from the citation network (71 citing papers 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(Cq) ÷ max(outdegreeq, 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 →

      Click a node to highlight its connections. Use scroll to zoom. Drag to pan.

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

      Authors (2)

      Ren XuORCID,Jun DuORCID

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