Metadata-Version: 2.4
Name: py_semtools
Version: 1.3.1
Summary: Library to handle ontologies that allows queries and calculations such as information coefficients, semantic similarity, ontology representations, etc in a easy way. It can load any ontology that complies the obo format supported by OBO Foundry.
Home-page: https://github.com/seoanezonjic/py_semtools
Author: seoanezonjic
Author-email: seoanezonjic@uma.es
License: MIT
Project-URL: Documentation, https://github.com/seoanezonjic/py_semtools
Platform: any
Classifier: Development Status :: 4 - Beta
Classifier: Programming Language :: Python
Description-Content-Type: text/x-rst; charset=UTF-8
License-File: LICENSE.txt
Requires-Dist: importlib-metadata; python_version < "3.8"
Requires-Dist: platformdirs
Requires-Dist: numpy
Requires-Dist: networkx
Requires-Dist: entrezpy
Requires-Dist: scipy
Requires-Dist: sentence_transformers
Requires-Dist: langchain
Requires-Dist: langchain-text-splitters
Requires-Dist: pynvml
Requires-Dist: requests
Requires-Dist: py_exp_calc
Requires-Dist: py_cmdtabs
Requires-Dist: py_report_html
Requires-Dist: loguru
Requires-Dist: loky
Provides-Extra: testing
Requires-Dist: setuptools; extra == "testing"
Requires-Dist: pytest; extra == "testing"
Requires-Dist: pytest-cov; extra == "testing"
Dynamic: license-file

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===========
py_semtools
===========


    Library to handle ontologies that allows queries and calculations (information coefficients, semantic similarity, ontology representations, etc) in a easy way. It can load any ontology that complies the obo format supported by OBO Foundry.


This library facilitates easy querying and calculations (information coefficients, semantic similarity, ontology representations, etc.) for ontologies. It supports any ontology that complies with the OBO format as endorsed by the OBO Foundry.

Key features of this library include:

* Ontology Queries: Perform term ID or name translations, search for the latest stable ID of a given term, infer term parents, and more.
* Association of Items to Terms: Load term profiles associated with items such as genes, patients, etc., and retrieve the most specific terms. Calculate information coefficients based on item frequency and determine semantic similarity against other items.
* Ontology Representations: Methods to represent the specificity and frequency of terms within a set of items, enabling visualization of the ontology distribution in a given dataset.
* Text Similarity Analysis: Utilize Sentence Transformers (SBERT) for text similarity analysis.

