Role of Nature-Inspired Intelligence in Genomic Diagnosis of Antimicrobial Resistance

Abstract

The emerging antimicrobial resistance (AMR) to current antimicrobial agents is the foremost public health concern that continues to pose challenges in the selection of therapeutic regimens to treat infectious diseases. The bacterial pathogens develop AMR by two types of mechanisms, one is intrinsic resistance due to the mutations in chromosomal genes and another is extrinsic resistance by the acquisition of external plasmid-mediated genes. The key to diagnose AMR lies in the DNA sequence of bacteria harboring the resistance-conferring mechanisms. The advancements in technology have generated a plethora of genomics data that can be utilized for the identification of diagnostic markers. Moreover, machine learning (ML) has created novel opportunities to significantly solve healthcare problems using bioinformatics techniques. In the last decade, nature-inspired intelligence (NII) has aided the development of machine learning tools for diagnosing antibacterial resistance gene patterns. The successful implementation of these algorithms, especially on complex and intricate problems, indicates their importance in artificial intelligence (AI). This review addresses the role of NII in combating infectious diseases using genomic data as well as the future perspective of its use in information processing, decision-making, and optimization for the diagnosis of AMR. The key problems in the practical application of NII using genomic markers and microbiological parameters are extensively discussed. This will aid in bridging the gap between theoretical researchers, medical practitioners, professionals, and engineers interested in the use of NII to solve AMR.