Enhancing Bacterial Phenotype Classification Through the Integration of Autogating and Automated Machine Learning in Flow Cytometric Analysis.

Abstract excerpt

Although flow cytometry produces reliable results, the data processing from gating to fingerprinting is prone to subjective bias. Here, we integrated autogating with Automated Machine Learning in flow cytometry to enhance the classification of bacterial phenotypes. We analyzed six bacterial strains prevalent in the soil and groundwater- Bacillus subtilis , Burkholderia thailandensis , Corynebacterium glutamicum ,...

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