Dataset Information

Metab2MeSH: annotating compounds with medical subject headings.

ABSTRACT:

Summary

Progress in high-throughput genomic technologies has led to the development of a variety of resources that link genes to functional information contained in the biomedical literature. However, tools attempting to link small molecules to normal and diseased physiology and published data relevant to biologists and clinical investigators, are still lacking. With metabolomics rapidly emerging as a new omics field, the task of annotating small molecule metabolites becomes highly relevant. Our tool Metab2MeSH uses a statistical approach to reliably and automatically annotate compounds with concepts defined in Medical Subject Headings, and the National Library of Medicine's controlled vocabulary for biomedical concepts. These annotations provide links from compounds to biomedical literature and complement existing resources such as PubChem and the Human Metabolome Database.

SUBMITTER: Sartor MA

PROVIDER: S-EPMC3348562 | biostudies-literature | 2012 May

REPOSITORIES: biostudies-literature

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Metab2MeSH: annotating compounds with medical subject headings.

Sartor Maureen A MA Ade Alex A Wright Zach Z States David D Omenn Gilbert S GS Athey Brian B Karnovsky Alla A

Bioinformatics (Oxford, England) 20120406 10

<h4>Summary</h4>Progress in high-throughput genomic technologies has led to the development of a variety of resources that link genes to functional information contained in the biomedical literature. However, tools attempting to link small molecules to normal and diseased physiology and published data relevant to biologists and clinical investigators, are still lacking. With metabolomics rapidly emerging as a new omics field, the task of annotating small molecule metabolites becomes highly relev ...[more]

PMID: 22492643

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Project description:BackgroundMedical Subject Headings (MeSH) thesaurus contribute towards efficient searching of biomedical information. However, insufficient coverage of specific fields and inaccuracies in the indexing of articles can lead to bias during literature retrieval.ObjectivesThis meta-research study aimed to assess the use of 'Pharmaceutical Services' MeSH terms in studies evaluating the effect of pharmacists' interventions.MethodsAn updated systematic search (Jan-2022) to gather meta-analyses comparing pharmacists' interventions vs. other forms of care was performed. All MeSH terms allocated to the MEDLINE record of each primary study included in the selected meta-analyses were systematically extracted. Terms from the 'Pharmaceutical Services' branch, including its descendants, as well as other 26 pharmacy-specific MeSH terms were identified. The assignment of these terms as a 'Major MeSH' was also evaluated. Descriptive statistics and social network analyses to evaluate the co-occurrence of the MeSH terms in the articles were conducted. Sensitivity analyses including only meta-analyses with declared objectives mentioning the words 'pharmacist' or 'pharmacy' were performed (SPSS v.24.0).ResultsOverall, 138 meta-analyses including 2012 primary articles were evaluated. A median of 15 [IQR 12-18] MeSH terms were assigned per article with a slight positive time-trend (Spearman rho = 0.193; p < 0.001). Only 36.6% (n = 736/2012) and 58.1% (n = 338/1099) of studies were indexed with one MeSH term from the 'Pharmaceutical Services' branch in the overall and sensitivity analyses, respectively. In <20% of cases, these terms were a 'Major MeSH'. The pharmacy-specific term 'Pharmacists' was the most frequently used, yet in only 27.8% and 47.7% of articles in the original and sensitivity analyses, respectively. Social networks showed a weak association between pharmacy-specific and 'Pharmaceutical services' branch MeSH terms.ConclusionsThe availability of a 'Pharmaceutical services' branch hierarchic tree and further pharmacy-specific MeSH terms incorporated to the MeSH thesaurus in the past years is not related with accurate indexing of articles.

Project description:BackgroundAs healthcare-related data proliferate, there is need to annotate them expertly for the purposes of personalized medicine. Crowdworking is an alternative to expensive expert labour. Annotation corresponds to diagnosis, so comparing unlabeled records to labeled ones seems more appropriate for crowdworkers without medical expertise. We modeled the comparison of a record to two other records as a triplet annotation task, and we conducted an experiment to investigate to what extend sensor-measured stress, task duration, uncertainty of the annotators and agreement among the annotators could predict annotation correctness.Materials and methodsWe conducted an annotation experiment on health data from a population-based study. The triplet annotation task was to decide whether an individual was more similar to a healthy one or to one with a given disorder. We used hepatic steatosis as example disorder, and described the individuals with 10 pre-selected characteristics related to this disorder. We recorded task duration, electro-dermal activity as stress indicator, and uncertainty as stated by the experiment participants (n = 29 non-experts and three experts) for 30 triplets. We built an Artificial Similarity-Based Annotator (ASBA) and compared its correctness and uncertainty to that of the experiment participants.ResultsWe found no correlation between correctness and either of stated uncertainty, stress and task duration. Annotator agreement has not been predictive either. Notably, for some tasks, annotators agreed unanimously on an incorrect annotation. When controlling for Triplet ID, we identified significant correlations, indicating that correctness, stress levels and annotation duration depend on the task itself. Average correctness among the experiment participants was slightly lower than achieved by ASBA. Triplet annotation turned to be similarly difficult for experts as for non-experts.ConclusionOur lab experiment indicates that the task of triplet annotation must be prepared cautiously if delegated to crowdworkers. Neither certainty nor agreement among annotators should be assumed to imply correct annotation, because annotators may misjudge difficult tasks as easy and agree on incorrect annotations. Further research is needed to improve visualizations for complex tasks, to judiciously decide how much information to provide, Out-of-the-lab experiments in crowdworker setting are needed to identify appropriate designs of a human-annotation task, and to assess under what circumstances non-human annotation should be preferred.

Dataset Information

Metab2MeSH: annotating compounds with medical subject headings.

Summary

Publications

Metab2MeSH: annotating compounds with medical subject headings.

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