#############################
## BioC 2.14 to BioC 3.13 ##
#############################
##
## library(MeSH.Hsa.eg.db)
## db <- MeSH.Hsa.eg.db
##
##---------------------------
# From BioC 3.14 (Nov. 2021, with R-4.2.0)
library(AnnotationHub)
library(MeSHDbi)
ah <- AnnotationHub(localHub=TRUE)
hsa <- query(ah, c("MeSHDb", "Homo sapiens"))
file_hsa <- hsa[[1]]
db <- MeSHDbi::MeSHDb(file_hsa)23 MeSH semantic similarity analysis
| Aspect | MeSH semantic similarity |
|---|---|
| Questions | How similar are MeSH terms or gene annotations under the MeSH hierarchy and information-content model? |
| Input | MeSH IDs, gene IDs, MeSH annotation data, and the semantic data prepared for meshes. |
| Methods | Information-content and graph-structure measures including Resnik, Lin, Jiang, Schlicker, and Wang. |
| Output | Term or gene similarity scores and matrices for grouping or downstream evidence integration. |
| Main limitations | Results depend on MeSH hierarchy, annotation frequency, database release, and identifier coverage. Similarity captures shared annotation structure, not clinical equivalence or causality. |
Use this chapter after checking MeSH coverage and release information; for the shared method overview, see semantic similarity.
MeSH (Medical Subject Headings) is the NLM (U.S. National Library of Medicine) controlled vocabulary used to manually index articles for MEDLINE/PubMed. MeSH is a comprehensive life science vocabulary. MeSH has 19 categories and MeSH.db contains 16 of them. That is:
| Abbreviation | Category |
|---|---|
| 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 and Food and Beverages |
| K | Humanities |
| L | Information Science |
| M | Persons |
| N | Health Care |
| V | Publication Type |
| Z | Geographical Locations |
MeSH terms were associated with Entrez Gene ID by three methods, gendoo, gene2pubmed and RBBH (Reciprocal Blast Best Hit).
| Method | Way of corresponding Entrez Gene IDs and MeSH IDs |
|---|---|
| Gendoo | Text-mining |
| gene2pubmed | Manual curation by NCBI teams |
| RBBH | sequence homology with BLASTP search (E-value<10-50) |
23.1 Supported organisms
The meshes package (Yu 2018) relies on MeSHDb to prepare semantic data for measuring similarity. MeSHDb can be downloaded from AnnotationHub (see also AHMeSHDbs) and about 200 species are available and are supported by the meshes package.
First, we need to load/fetch species-specific MeSH annotation database:
The semantic data can be prepared by the meshdata() function:
library(meshes)
hsamd <- meshdata(db, category='A', computeIC=T, database="gendoo")
## you may want to save the result for future usage
#
# save(hsamd, file = "hsamd.rda")
#23.2 MeSH semantic similarity measurement
The meshes package (Yu 2018) implemented four IC-based methods (i.e. Resnik (Philip 1999), Jiang (Jiang and Conrath 1997), Lin (Lin 1998) and Schlicker (Schlicker et al. 2006)) and one graph-structure based method (i.e. Wang (Wang et al. 2007)), to measure MeSH term semantic similarity. For algorithm details, please refer to Chapter 1.
The meshSim() function is designed to measure semantic similarity between two MeSH term vectors.
library(meshes)
meshSim("D000009", "D009130", semData=hsamd, measure="Resnik")[1] 0.3847944meshSim("D000009", "D009130", semData=hsamd, measure="Rel")[1] 0.633538meshSim("D000009", "D009130", semData=hsamd, measure="Jiang")[1] 0.5587351meshSim("D000009", "D009130", semData=hsamd, measure="Wang")[1] 0.5557103meshSim(c("D001369", "D002462"), c("D017629", "D002890", "D008928"), semData=hsamd, measure="Wang") D017629 D002890 D008928
D001369 0.2886598 0.1923711 0.2193326
D002462 0.6521739 0.2381925 0.280955223.3 Gene semantic similarity measurement
The geneSim() function is designed to measure semantic similarity among two gene vectors.
geneSim("241", "251", semData=hsamd, measure="Wang", combine="BMA")[1] 0.216geneSim(c("241", "251"), c("835", "5261","241", "994"), semData=hsamd, measure="Wang", combine="BMA") 835 5261 241 994
241 0.353 0.143 1.000 0.262
251 0.301 0.347 0.216 0.36123.4 Next steps
- Apply the MeSH resource to MeSH enrichment.
- Use the common visualization map for returned result objects.
References
Jiang, Jay J., and David W. Conrath. 1997. “Semantic Similarity Based on Corpus Statistics and Lexical Taxonomy.” Proceedings of 10th International Conference on Research In Computational Linguistics. http://www.citebase.org/abstract?id=oai:arXiv.org:cmp-lg/9709008.
Lin, Dekang. 1998. “An Information-Theoretic Definition of Similarity.” In Proceedings of the 15th International Conference on Machine Learning, 296—304. https://doi.org/10.1.1.55.1832.
Philip, Resnik. 1999. “Semantic Similarity in a Taxonomy: An Information-Based Measure and Its Application to Problems of Ambiguity in Natural Language.” Journal of Artificial Intelligence Research 11: 95–130.
Schlicker, Andreas, Francisco S Domingues, Jörg Rahnenführer, and Thomas Lengauer. 2006. “A New Measure for Functional Similarity of Gene Products Based on Gene Ontology.” BMC Bioinformatics 7: 302. https://doi.org/1471-2105-7-302.
Wang, James Z, Zhidian Du, Rapeeporn Payattakool, Philip S Yu, and Chin-Fu Chen. 2007. “A New Method to Measure the Semantic Similarity of GO Terms.” Bioinformatics (Oxford, England) 23 (May): 1274–81. https://doi.org/btm087.
Yu, Guangchuang. 2018. “Using Meshes for MeSH Term Enrichment and Semantic Analyses.” Bioinformatics 34 (21): 3766–67. https://doi.org/10.1093/bioinformatics/bty410.