Metadata-Version: 2.1
Name: minmlst
Version: 0.3.2
Summary: Machine-learning based minimal MLST scheme for bacterial strain typing
Home-page: https://github.com/shanicohen33/minMLST
Author: Shani Cohen
Author-email: shani.cohen.33@gmail.com
License: UNKNOWN
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.6
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: Microsoft :: Windows
Classifier: Operating System :: POSIX :: Linux
Description-Content-Type: text/markdown
Requires-Dist: shap (>=0.28.5)
Requires-Dist: xgboost (>=0.82)
Requires-Dist: dill (>=0.3.0)
Requires-Dist: scikit-learn (==0.20.1)

minMLST is a machine-learning based methodology for identifying a minimal subset of genes that preserves high discrimination among bacterial strains. It combines well known machine-learning algorithms and approaches such as XGBoost, distance-based hierarchical clustering, and SHAP. 
minMLST quantifies the importance level of each gene in an MLST scheme and allows the user to investigate the trade-off between minimizing the number of genes in the scheme vs preserving a high resolution among strains.

 See more information in [GitHub](https://github.com/shanicohen33/minMLST).

