Artificial intelligence tools for the identification of antibiotic resistance genes - Olatunji, Isaac; Bardaji, Danae Kala Rodriguez; Miranda, Renata Rezende; Savka, Michael A.; Hudson, André O. (Frontiers in Microbiology, 2024)
The fight against bacterial antibiotic resistance must be given critical attention to avert the current and emerging crisis of treating bacterial infections due to the inefficacy of clinically relevant antibiotics. Intrinsic genetic mutations and transferrable Antibiotic Resistance Genes (ARGs) are at the core of the development of antibiotic resistance. However, traditional alignment methods for detecting ARGs have limitations. Artificial intelligence (AI) methods and approaches can potentially augment the detection of ARGs and identify antibiotic targets and antagonistic bactericidal and bacteriostatic molecules that are or can be developed as antibiotics. This review delves into the literature regarding the various AI methods and approaches for identifying and annotating ARGs, highlighting their potential and limitations. Specifically, we discuss methods for 1) direct identification and classification of ARGs from genome DNA sequences, 2) direct identification and classification from plasmid sequences, and 3) identification of putative ARGs from feature selection.