Antibiotics have transformed modern medicine in manifold ways. However, the misuse and over-consumption of antibiotics or antimicrobials have led to the rise in antimicrobial resistance (AMR). Unfortunately, robust tools or techniques for the detection of potential loci responsible for AMR before it happens are lacking. The emergence of resistance even when a strain lacks known AMR genes has puzzled researchers for a long time. Clearly, there is a critical need for the development of novel approaches for uncovering yet unknown resistance elements in pathogens and advancing our understanding of emerging resistance mechanisms. To aid in the development of new tools for deciphering AMR, here we propose a machine learning (ML) based framework that provides ML models trained and tested on (1) genotypic AMR and phenotypic antimicrobial susceptibility testing (AST) data, which can predict novel resistance factors in bacterial strains that lack already implicated resistance genes; and (2) complete gene set and AST phenotypic data, which can predict the most important genetic loci involved in resistance to specific antibiotics in bacterial strains. The validation of resistance loci prioritized by our ML pipeline was performed using homology modeling and in silico molecular docking.
| Identifer | oai:union.ndltd.org:unt.edu/info:ark/67531/metadc1985530 |
| Date | 08 1900 |
| Creators | Sunuwar, Janak |
| Contributors | Azad, Rajeev, Shulaev, Vladimir, Mikler, Armin, Jagadeeswaran, Pudur, Allen, Michael, Padilla, Pamela |
| Publisher | University of North Texas |
| Source Sets | University of North Texas |
| Language | English |
| Detected Language | English |
| Type | Thesis or Dissertation |
| Format | Text |
| Rights | Public, Sunuwar, Janak, Copyright, Copyright is held by the author, unless otherwise noted. All rights Reserved. |
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