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CC BY V4.0, Open Access
Journal article
PARGT: a software tool for predicting antimicrobial resistance in bacteria
Scientific Reports, Vol.10(11033)
07/2020
Handle:
https://hdl.handle.net/2376/17919
Abstract
With the ever-increasing availability of whole-genome sequences, machine-learning approaches can be used as an alternative to traditional alignment-based methods for identifying new antimicrobial-resistance genes. Such approaches are especially helpful when pathogens cannot be cultured in the lab. In previous work, we proposed a game-theory-based feature evaluation algorithm. When using the protein characteristics identified by this algorithm, called ‘features’ in machine learning, our model accurately identified antimicrobial resistance (AMR) genes in Gram-negative bacteria. Here we extend our study to Gram-positive bacteria showing that coupling game-theory-identified features with machine learning achieved classification accuracies between 87% and 90% for genes encoding resistance to the antibiotics bacitracin and vancomycin. Importantly, we present a standalone software tool that implements the game-theory algorithm and machine-learning model used in these studies.
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Details
- Title
- PARGT: a software tool for predicting antimicrobial resistance in bacteria
- Creators
- Abu Sayed Chowdhury (Author)Douglas R. Call (Author)Shira L. Broschat (Author)
- Publication Details
- Scientific Reports, Vol.10(11033)
- Academic Unit
- Electrical Engineering and Computer Science, School of; Paul G. Allen School for Global Animal Health
- Identifiers
- 99900501907601842
- Copyright
- Creative Commons Attribution 4.0 International ; https://creativecommons.org/licenses/by/4.0/
- Language
- English
- Resource Type
- Journal article