Experiment Overview
| Repository ID: | FR-FCM-Z75D | Experiment name: | Automated Machine Learning in Flow Cytometry | MIFlowCyt score: | 45.50% |
| Primary researcher: | Young Jun Bae | PI/manager: | Young Jun Bae | Uploaded by: | Young Jun Bae |
| Experiment dates: | 2022-03-11 - 2022-07-19 | Dataset uploaded: | Jan 2024 | Last updated: | Apr 2024 |
| Keywords: | [Classification] [auto-gating ] [Phenotypic fingerprinting] [metabolic phase] [gradient boosting machine] | Manuscripts: | |||
| Organizations: |
Yonsei University, Yonsei University, Wonju-si, Gang'weondo (South Korea)
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| Purpose: | The primary objective of our study was to accurately identify and distinguish the unique flowcytometric phenotypic fingerprints of bacterial cells from defined cultures, reducing subjective bias through the use of autogating and AutoML techniques. | ||||
| Conclusion: | None | ||||
| Comments: | bs: Bacillus subtilis subsp. spizizenii bt: Burkholderia thailandensis cg: Corynebacterium glutamicum ec: Escherichia coli pp: Pseudomonas putida ps: pseudomonas stutzeri | ||||
| Funding: | Not disclosed | ||||
| Quality control: | None | ||||
