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Benchmarking Genomic Encodings For AMR Prediction: The Superiority of K-Mers and Ensemble Learning Over Deep Learning by baophan2362005 is a document available to read on EtoBox.

This study evaluates various genomic encoding methods for predicting antimicrobial resistance (AMR) in Escherichia coli, comparing traditional K-mer counting with ensemble learning, SNP-based methods, and deep learning approaches. Results indicate that K-mer frequency profiles, particularly using 4-mers with gradient boosting algorithms, outperform deep learning models in accuracy and robustness for predicting resistance to antibiotics like gentamicin and ampicillin. The findings highlight the limitations o

Author

baophan2362005

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EN