Abstract
Genome-wide association study techniques used on bacterial genomes have produced encouraging findings for the identification of genetic markers or a thorough evaluation of marker effect. K-mer composition-based alignment-free techniques have recently demonstrated their capacity to investigate the accessory genome. The gonorrhea-causing bacterium Neisseria gonorrhoeae will be the main focus of this book chapter’s analysis of antibiotic resistance in bacteria. The second most prevalent STI (sexually transmitted infection) in Europe is gonorrhea, behind chlamydia. Infection rates for gonorrhea are rising, with a 26% increase in the UK from 2017 to 2018. The disease spreads because many sick people, especially women, don’t show any symptoms. If the infection is not treated, it may cause infertility in females, and, in rare cases, it may spread to your joints, heart valves, brain, or spinal cord. As these bacteria develop an increased level of antibiotic resistance, illnesses become more challenging to cure. Data on genome sequence and antibiotic resistance have been compiled from a variety of freely accessible sources. We are employing unitigs, brief DNA segments shared by a portion of the strains in our study, for this analysis. We will examine machine learning algorithms for predicting azithromycin resistance in this book chapter.
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