ArticleBMC bioinformatics2012
Quantitative biomedical annotation using medical subject heading over-representation profiles (MeSHOPs).
Article in BMC bioinformatics, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
17 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A review of validation strategies for computational drug repositioning.Briefings in bioinformatics · 2018Pooled it
- Glutaminase Deficiency Caused by Short Tandem Repeat Expansion inThe New England journal of medicine · 2019Article
- Novel application of normalized pointwise mutual information (NPMI) to mine biomedical literature for gene sets associated with disease: use case in breast carcinogenesis.Computational toxicology (Amsterdam, Netherlands) · 2018Article
- Aberration hubs in protein interaction networks highlight actionable targets in cancer.Oncotarget · 2018Article
- Leveraging Population-Based Clinical Quantitative Phenotyping for Drug Repositioning.CPT: pharmacometrics & systems pharmacology · 2018Observational
- BCScreen: A gene panel to test for breast carcinogenesis in chemical safety screening.Computational toxicology (Amsterdam, Netherlands) · 2018Article
- 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
- Exploring the cellular basis of human disease through a large-scale mapping of deleterious genes to cell types.Genome medicine · 2015Article
- An application of MeSH enrichment analysis in livestock.Animal genetics · 2015Article
- ANDSystem: an Associative Network Discovery System for automated literature mining in the field of biology.BMC systems biology · 2015Article
- Molecular association of pathogenetic contributors to pre-eclampsia (pre-eclampsia associome).BMC systems biology · 2015Article
- Compensating for literature annotation bias when predicting novel drug-disease relationships through Medical Subject Heading Over-representation Profile (MeSHOP) similarity.BMC medical genomics · 2013Article
- Article
- Mining the literature: new methods to exploit keyword profiles.Genome medicine · 2012Article
- Inferring novel gene-disease associations using Medical Subject Heading Over-representation Profiles.Genome medicine · 2012Article
- A literature search tool for intelligent extraction of disease-associated genes.Journal of the American Medical Informatics Association : JAMIAArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
backgroundMEDLINE®/PubMed® indexes over 20 million biomedical articles, providing curated annotation of its contents using a controlled vocabulary known as Medical Subject Headings (MeSH). The MeSH vocabulary, developed over 50+ years, provides a broad coverage of topics across biomedical research. Distilling the essential biomedical themes for a topic of interest from the relevant literature is important to both understand the importance of related concepts and discover new relationships.
resultsWe introduce a novel method for determining enriched curator-assigned MeSH annotations in a set of papers associated to a topic, such as a gene, an author or a disease. We generate MeSH Over-representation Profiles (MeSHOPs) to quantitatively summarize the annotations in a form convenient for further computational analysis and visualization. Based on a hypergeometric distribution of assigned terms, MeSHOPs statistically account for the prevalence of the associated biomedical annotation while highlighting unusually prevalent terms based on a specified background. MeSHOPs can be visualized using word clouds, providing a succinct quantitative graphical representation of the relative importance of terms. Using the publication dates of articles, MeSHOPs track changing patterns of annotation over time. Since MeSHOPs are quantitative vectors, MeSHOPs can be compared using standard techniques such as hierarchical clustering. The reliability of MeSHOP annotations is assessed based on the capacity to re-derive the subset of the Gene Ontology annotations with equivalent MeSH terms.
conclusionsMeSHOPs allows quantitative measurement of the degree of association between any entity and the annotated medical concepts, based directly on relevant primary literature. Comparison of MeSHOPs allows entities to be related based on shared medical themes in their literature. A web interface is provided for generating and visualizing MeSHOPs.
Indexed as
Identifiers
What 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.