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• W4224019874 abstract "Bacterial pathogen identification, which is critical for human health, has historically relied on culturing organisms from clinical specimens. More recently, the application of machine learning (ML) to whole-genome sequences (WGSs) has facilitated pathogen identification. However, relying solely on genetic information to identify emerging or new pathogens is fundamentally constrained, especially if novel virulence factors exist. In addition, even WGSs with ML pipelines are unable to discern phenotypes associated with cryptic genetic loci linked to virulence. Here, we set out to determine if ML using phenotypic hallmarks of pathogenesis could assess potential pathogenic threat without using any sequence-based analysis. This approach successfully classified potential pathogenetic threat associated with previously machine-observed and unobserved bacteria with 99% and 85% accuracy, respectively. This work establishes a phenotype-based pipeline for potential pathogenic threat assessment, which we term PathEngine, and offers strategies for the identification of bacterial pathogens." @default.
• W4224019874 created "2022-04-19" @default.
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• W4224019874 date "2022-04-01" @default.
• W4224019874 modified "2023-10-01" @default.
• W4224019874 title "Phenotype-Based Threat Assessment" @default.
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• W4224019874 doi "https://doi.org/10.1073/pnas.2112886119" @default.
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• W4224019874 hasPublicationYear "2022" @default.
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