Antimicrobial resistance genetic factor identification from whole-genome sequence data using deep feature selection.

ABSTRACT:

Background

Antimicrobial resistance (AMR) is a major threat to global public health because it makes standard treatments ineffective and contributes to the spread of infections. It is important to understand AMR's biological mechanisms for the development of new drugs and more rapid and accurate clinical diagnostics. The increasing availability of whole-genome SNP (single nucleotide polymorphism) information, obtained from whole-genome sequence data, along with AMR profiles provides an opportunity to use feature selection in machine learning to find AMR-associated mutations. This work describes the use of a supervised feature selection approach using deep neural networks to detect AMR-associated genetic factors from whole-genome SNP data.

Results

The proposed method, DNP-AAP

SUBMITTER: Shi J 

PROVIDER: S-EPMC6929425 | biostudies-literature | 2019 Dec

REPOSITORIES: biostudies-literature