PARGT: a software tool for predicting antimicrobial resistance in bacteria
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 t...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2020-07-01
|
| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-020-67949-9 |
| _version_ | 1818683401305063424 |
|---|---|
| author | Abu Sayed Chowdhury Douglas R. Call Shira L. Broschat |
| author_facet | Abu Sayed Chowdhury Douglas R. Call Shira L. Broschat |
| author_sort | Abu Sayed Chowdhury |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-17T10:34:09Z |
| format | Article |
| id | doaj.art-fecbf25a3723448bb1b91406076c2eaf |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-12-17T10:34:09Z |
| publishDate | 2020-07-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-fecbf25a3723448bb1b91406076c2eaf2022-12-21T21:52:27ZengNature PortfolioScientific Reports2045-23222020-07-011011710.1038/s41598-020-67949-9PARGT: a software tool for predicting antimicrobial resistance in bacteriaAbu Sayed Chowdhury0Douglas R. Call1Shira L. Broschat2School of Electrical Engineering and Computer Science, Washington State UniversitySchool of Electrical Engineering and Computer Science, Washington State UniversitySchool of Electrical Engineering and Computer Science, Washington State UniversityAbstract 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.https://doi.org/10.1038/s41598-020-67949-9 |
| spellingShingle | Abu Sayed Chowdhury Douglas R. Call Shira L. Broschat PARGT: a software tool for predicting antimicrobial resistance in bacteria Scientific Reports |
| title | PARGT: a software tool for predicting antimicrobial resistance in bacteria |
| title_full | PARGT: a software tool for predicting antimicrobial resistance in bacteria |
| title_fullStr | PARGT: a software tool for predicting antimicrobial resistance in bacteria |
| title_full_unstemmed | PARGT: a software tool for predicting antimicrobial resistance in bacteria |
| title_short | PARGT: a software tool for predicting antimicrobial resistance in bacteria |
| title_sort | pargt a software tool for predicting antimicrobial resistance in bacteria |
| url | https://doi.org/10.1038/s41598-020-67949-9 |
| work_keys_str_mv | AT abusayedchowdhury pargtasoftwaretoolforpredictingantimicrobialresistanceinbacteria AT douglasrcall pargtasoftwaretoolforpredictingantimicrobialresistanceinbacteria AT shiralbroschat pargtasoftwaretoolforpredictingantimicrobialresistanceinbacteria |