Machine learning and feature extraction for rapid antimicrobial resistance prediction of from whole-genome sequencing data.

Journal: Frontiers in microbiology
Published Date: Jan 11, 2024

Abstract

BACKGROUND: Whole-genome sequencing (WGS) has contributed significantly to advancements in machine learning methods for predicting antimicrobial resistance (AMR). However, the comparisons of different methods for AMR prediction without requiring prior knowledge of resistance remains to be conducted.

Authors

  • Yue Gao
    Institute of Medical Technology, Peking University Health Science Center, Beijing, China.
  • Henan Li
    Department of Clinical Laboratory, Peking University People's Hospital, Beijing, China.
  • Chunjiang Zhao
    Department of Clinical Laboratory, Peking University People's Hospital, Beijing, China.
  • Shuguang Li
    Department of Clinical Laboratory, Peking University People's Hospital, Beijing, China.
  • Guankun Yin
    Department of Clinical Laboratory, Peking University People's Hospital, Beijing, China.
  • Hui Wang
    Department of Vascular Surgery, Xuanwu Hospital, Capital Medical University, Beijing, China.

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