Machine-learning based minimal MLST scheme for bacterial strain typing
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.
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
|Filename, size||File type||Python version||Upload date||Hashes|
|Filename, size minmlst-0.3.4-py3-none-any.whl (13.9 kB)||File type Wheel||Python version py3||Upload date||Hashes View|
|Filename, size minmlst-0.3.4.tar.gz (12.8 kB)||File type Source||Python version None||Upload date||Hashes View|