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Related Experiment Video

Updated: Dec 18, 2025

Cloud-Based Phrase Mining and Analysis of User-Defined Phrase-Category Association in Biomedical Publications

Cloud-Based Phrase Mining and Analysis of User-Defined Phrase-Category Association in Biomedical Publications

Published on: February 23, 2019

pyMeSHSim: an integrative python package for biomedical named entity recognition, normalization, and comparison of

Zhi-Hui Luo^1,2, Meng-Wei Shi^1,2, Zhuang Yang^1,2

¹Hubei Key Laboratory of Agricultural Bioinformatics, College of Life Science and Technology, Huazhong Agricultural University, Wuhan, Hubei, 430070, PR China.

BMC Bioinformatics

|June 20, 2020

Summary

pyMeSHSim enhances biomedical named entity recognition and semantic similarity analysis for disease phenotypes. This tool improves data integration and system genetics by accurately mapping and comparing biomedical terms.

Keywords:

Disease MeSH Named entity recognition Semantic similarity Supplementary concept records UMLS

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Area of Science:

Bioinformatics
Computational Biology
Medical Informatics

Background:

Lack of uniform biomedical named entity (bio-NE) annotations for disease phenotypes.
Importance of semantic similarity comparison for data integration and system genetics.

Purpose of the Study:

To develop an integrative tool, pyMeSHSim, for bio-NE recognition, normalization, and semantic similarity comparison.
To improve the accuracy of mapping and comparing biomedical terms related to disease phenotypes.

Main Methods:

Utilized MetaMap for bio-NE recognition, producing Unified Medical Language System (UMLS) concepts.
Developed a house-made dataset of Medical Subject Headings (MeSH) main headings (MHs) and supplementary concept records (SCRs).
Implemented information content (IC)-based and graph-based algorithms for semantic similarity measurement.

Main Results:

pyMeSHSim demonstrated improved performance in recognizing OMIM phenotypes using SCRs and curated non-MeSH-synonymous UMLS concepts.
Achieved high performance in curating 461 GWAS phenotypes (recall >0.94, precision >0.56, F1 >0.70), outperforming DNorm and TaggerOne.
High correlation (0.89-0.99) observed between pyMeSHSim's semantic similarity analysis and manual curation.

Conclusions:

pyMeSHSim integrates MeSH MHs and SCRs for comprehensive bio-NE recognition, normalization, and comparison.
The tool facilitates biomedical text-mining and analysis of disease phenotype data.
pyMeSHSim offers a robust solution for semantic similarity assessment in bioinformatics.