PySML: An open Python library implementing semantic similarity measures

Maintainer: Gaston K. Mazandu^1,2,3 <gaston.mazandu at uct.ac.za>, Bioinformatics and Genomics Informatics Tools

(1) Computational Biology (CBIO) Group, Department of integrative Biomedical Sciences, Institute of Infectious Disease and Molecular Medicine (IDM), University of Cape Town (UCT), Observatory 7925, South Africa

(2) African Institute for Mathematical Sciences (AIMS), Melrose Road, Muizenberg 7945, South Africa

(3) Division of Human Genetics, Department of Pathology, Institute of Infectious Disease and Molecular Medicine (IDM), University of Cape Town (UCT), Observatory 7925, South Africa

The PySML Interface

PySML is a portable and expandable Python library enabling the retrieval of Semantic Similarity (SS) scores for any ontology to overcome issues related to computation, reproducibility and reusability of SS scores for any ontology in any application. PySML implements a large collection of SS measures consisting of 9 information content (IC) approaches, yielding 426 ontology concept and 3094 entity pair-wise SS measures (See Figure below).

As a library, PySML can be imported in a Python module, however, a user can also directly retrieve SS scores or run embedded applications in two main steps: User interface and input processing via a simple single command-line terminal. SS scores produced are presented in a table format, displayed on the screen or directed into a file.

The PySML Documentation

To view the documentation for the version of the PySML library downloaded in your system, go to the pysml directory and type the following command line:

python setup.py --long-description

Refer to the PDF version of the documentation below for more information on the PySML library administration and usage, as well as information on different IC, concept and entity SS models, as well as associated common applications implemented in this library.

TXT	Show Library Source	PySML
PDF		Reference Manual
Code	pysml-tool.tar.gz	Python Source Code

The PySML library can also be retrieved from the github public platform using git clone command line as follows.

git clone https://github.com/gkm-software-dev/post-analysis-tools.git

PySML Requirement Details

Description	An open, portable and expandable Python library implementing existing semantic similarity measures for any ontology, as well as common related applications.
Version	2.5.1
Depends	Python (≥ 2.7.x)
License	GPL (>= 3)
Requires	python-networkx library, [python-scipy, python-matplotlib]
SystemRequirements	OS Independent, but tested only on Linux (Ubuntu)
Release date	Thursday 30^th Aprily, 2020 −12:00

Functions and tools included»

The main functions and tools in the IHP-PING Interface are:

informationcontent module implementing a higher class for retrieving IC scores.
conceptsimilary module implementing a class inheriting InformationContent class to compute concept or term pair-wise semantic similarity scores.
entitysimilary module implementing a class inheriting ConceptSimilarity class to calculate entity pair-wise semantic similarity scores.
smlapps folder containing modules implementing common applications related to semantic similarity measures.
imports folder containing imported modules for reading an ontology and outputting different results.

Important notes:

Currently, PySML include all existing semantic similarity models and provides a possiblity to implement annotation-based or a new IC approach.
PySML deals with any ontology, independently of the file format (OBO, OWL or RDF), whether the file is already stored on a local computer or server, or to be directly retrieved via an online source provided a URL.

Package Archives»

Follow PySML administration and usage instructions in the PDF and txt documentation files to optimally use this python package.

Tool Source Code	pysml-tool.tar.gz
Browse other tools	IHP-PING package
	FRANC tool
	A-DaGO-Fun Package

Tool website visitors»

Statistics since April 2020

Mailing Lists»

An appropriate PySML forum is still to be implemented, for now, any problem accounted when using this tool or comments should be forwarded by email to the PySML team:

Acknowledgements

Any work dependent on open-source software owes debt to those who developed these tools. The authors thank everyone involved with free software, from the core developers to those who contributed to the documentation. Many thanks to the authors of the freely available libraries for making this work possible. This study is supported by the National Institutes of Health (NIH), USA, under Common Fund under H3ABioNet (U24HG006941) and SADaCC (1U01HG007459-01).