More details can be seen in the reference.
cite：Luo, Z., Shi, M., Yang, Z. et al. pyMeSHSim: an integrative python package for biomedical named entity recognition, normalization, and comparison of MeSH terms. BMC Bioinformatics 21, 252 (2020). https://doi.org/10.1186/s12859-020-03583-6
Some dependence of pyMeSHSim had restrict version limitation. The most convenient way to install pyMeSHSim is that use conda create a python 3.6.5 enviroment, and then isntall pyMeSHSim in this enviroment.
The recognition and normalization of bio-NE, especially for diseases, play an important role in clinical and biomedical research, such as clinical decision support, cohort identification, pharmacovigilance, and drug repositioning. For example, bio-NE recognition and normalization are prerequisites for semantic analysis, including semantic comparison of bio-NEs in drug repositioning. However, there are multiple synonyms, abbreviations and variations for bio-NEs, making it challenging to curate bio-NEs from free biomedical text or clinical narrative text.
We extracted bio-NEs from free biomedical text and measured semantic similarity between the bio-NEs based on the Medical Subject Headings(MeSH).
MeSH is a medical vocabulary resource curated by the National Library of Medicine (NLM). It provides a hierarchically-organized terminology for indexing and cataloging of biomedical information in MEDLINE/PubMed and other NLM databases. Moreover, MeSH is organized as a directed acyclic graph, laying the foundation for computing semantic similarities between two concepts.
Although MeSH has potential for bio-NE recognition, normalization, and comparison , there is still a lack of MeSH tools to automatically recognize bio-NEs from free text and measure the semantic similarity between bio-NEs after normalization.
Here, we developed an integrative, lightweight and data-rich python package named pyMeSHSim to curate MeSH terms from free text and measure the semantic similarity between the MeSH terms.
- data subpackage
- The data subpackage has reorganized the MeSH information in bcolz format.
- It is lightweight and data-rich.
- It contained the main heading concepts, unique DescriptorUI, MeSH Tree code, and correspond UMLS ID.
- It contained all narrow concepts of the main heading concepts. It reserved the parent-child relationships and RN/RB relationships for all concepts.
- metamapWrap subpackage
- It provided some filter rules for parsing the free text.
- It provided a unified interface to create the MeSH concept objects.
- Sim subpackage
- It provided useful APIs to retrieve the MeSH dataset.
- It implemented four methods of semantic similarity measures based on information content.It implemented one method of semantic similarity measures based on path.
More details can be seen in the reference.
Software
- MetaMap 2016v2
Python packages
- python 3.6
- pandas
- bcolz>=1.2.1
You need to activate a UMLS Terminology Services (UTS) account to fetch MetaMap. Please see MetaMap for more information.
MetaMap depends on Java. To install openJDK:
$ sudo apt install default-jdkAfter downloading MetaMap and Extracting it, you can install it by:
$ cd ./public_mm/ $ bash ./bin/install.sh
At this point, you have successfully installed MetaMap.
Please add the bin directory to the environment variable PATH in bashrc for convenience:
$ export PATH=$PATH:/path_to_MetaMap/binThen, launch MetaMap server before running pyMeSHSim:
$ skrmedpostctl start $ wsdserverctl start
To install python package pandas and bcolz:
$ pip3 install pandas $ pip3 install bcolz
To install pyMeSHSim from source code:
$ git clone https://github.com/luozhhub/pyMeSHSim.git $ cd pyMeSHSim $ python3 ./setup.py install