MeSH Enrichment and Semantic Analyses

Overview

meshes provides tools for MeSH (Medical Subject Headings) enrichment and semantic similarity analysis. MeSH is the controlled vocabulary used by the National Library of Medicine to index biomedical literature, and it provides a structured way to describe diseases, chemicals, anatomy, organisms, techniques and other biomedical concepts.

The package links MeSH terms to Entrez Gene identifiers and supports workflows for both enrichment analysis and semantic comparison.

For worked examples and extended tutorials, please see the online book: https://yulab-smu.top/biomedical-knowledge-mining-book/.

Supported Data

meshes works with MeSH annotation data for more than 70 species. Gene-to-MeSH associations can be derived from the following sources:

• gendoo
• gene2pubmed
• RBBH

The package supports standard MeSH categories:

• A: Anatomy
• B: Organisms
• C: Diseases
• D: Chemicals and Drugs
• E: Analytical, Diagnostic and Therapeutic Techniques and Equipment
• F: Psychiatry and Psychology
• G: Phenomena and Processes
• H: Disciplines and Occupations
• I: Anthropology, Education, Sociology and Social Phenomena
• J: Technology, Industry and Agriculture
• K: Humanities
• L: Information Science
• M: Named Groups
• N: Health Care
• V: Publication Characteristics
• Z: Geographicals

For semantic similarity analysis, annotation data are prepared with meshdata(). The resulting object stores the selected MeSH category, gene-to-MeSH annotations and, optionally, information content values.

Supported Analyses

The main analysis tasks are:

• Over-representation analysis with enrichMeSH().
• Gene set enrichment analysis with gseMeSH().
• Semantic similarity between MeSH terms with meshSim().
• Semantic similarity between genes or gene groups with geneSim().

Semantic similarity can be calculated with the following methods:

• Wang
• Resnik
• Rel
• Jiang
• Lin

Gene-level semantic similarity can combine multiple MeSH term scores with:

• max
• avg
• rcmax
• BMA

Learn More

The package vignette intentionally keeps the local documentation concise. Full examples, analysis workflows and interpretation guidance are maintained in the online book:

• https://yulab-smu.top/biomedical-knowledge-mining-book/

Citation

If you use meshes in published research, please cite:

Yu G. Using meshes for MeSH term enrichment and semantic analyses. Bioinformatics. 2018, 34(21):3766-3767. https://doi.org/10.1093/bioinformatics/bty410.

Getting Help

For questions or issues, please post to the Bioconductor support site and tag your post with meshes.