Systematic Evaluation of Drug–Disease Relationships to Identify Leads for Novel Drug Uses
Systematic Evaluation of Drug–Disease Relationships to Identify Leads for Novel Drug Uses is a research paper published in Clinical Pharmacology & Therapeutics (2009). On theSindex it has a DataRank of 0.842. It has been cited 273 times.
Abstract
Drug repositioning refers to the discovery of alternative uses for drugs--uses that are different from that for which the drugs were originally intended. One challenge in this effort lies in choosing the indication for which a drug of interest could be prospectively tested. We systematically evaluated a drug treatment-based view of diseases in order to address this challenge. Suggestions for novel drug uses were generated using a "guilt by association" approach. When compared with a control group of drug uses, the suggested novel drug uses generated by this approach were significantly enriched with respect to previous and ongoing clinical trials.
›Data sources & pipeline
FAIR Checklist
Context only (not used in score)- Has DOI
- Open Access
FAIR checklist signals are shown for context only and do not affect DataRank scoring.
DataRank Breakdown
Base Score Contribution
0.842
From this paper's citation signal
Citation Network Contribution
0
Citation network not refreshed for this result
This paper's DataRank is currently driven only by its base citation score. Citation network data was not refreshed for this result.
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 100% comes from its base citations and 0% from the citation network.
- 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.