Machine-learning based minimal MLST scheme for bacterial strain typing
Project description
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.
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