ArticleAMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science2015
Concept Modeling-based Drug Repositioning.
Article in AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
4 citing papers in PubMed, 1 synthesis or guideline pooled it, 7 citations in OpenAlex.
- A review of validation strategies for computational drug repositioning.Briefings in bioinformatics · 2018Pooled it
- Leveraging Population-Based Clinical Quantitative Phenotyping for Drug Repositioning.CPT: pharmacometrics & systems pharmacology · 2018Observational
- MeSHDD: Literature-based drug-drug similarity for drug repositioning.Journal of the American Medical Informatics Association : JAMIA · 2017Article
- A standard database for drug repositioning.Scientific data · 2017Article
Corrections and comments
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Our hypothesis is that drugs and diseases sharing similar biomedical and genomic concepts are likely to be related, and thus repositioning opportunities can be identified by ranking drugs based on the incidence of shared similar concepts with diseases and vice versa. To test this, we constructed a probabilistic topic model based on the Unified Medical Language System (UMLS) concepts that appear in the disease and drug related abstracts in MEDLINE. The resulting probabilistic topic associations were used to measure the similarity between disease and drugs. The success of the proposed model is evaluated using a set of repositioned drugs, and comparing a drug's ranking based on its similarity to the original and new indication. We then applied the model to rare disorders and compared them to all approved drugs to facilitate "systematically serendipitous" discovery of relationships between rare diseases and existing drugs, some of which could be potential repositioning candidates.
Identifiers
26306277PMC4525261W1186508908What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.