Machine learning-based prediction of acquired antimicrobial resistance in multiple bacterial species using K-mer analysis, mutation detection, and AMR gene profiling

Ahmed, Md. SabbirRabiul Alam, Dr. Md. GolamHalder, Asit KumarBiswas, ArnabSaha, MoyuriSadi, Shaoukh Mazher2025-06-162025-06-1620252025-08ID: 20301247ID: 20301348ID: 20301277ID: 21101112http://hdl.handle.net/10361/26036Cataloged from PDF version of thesis.Includes bibliographical references (pages 87-88).This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2025.Antimicrobial resistance (AMR) is a major global health concern, necessitating rapid and accurate prediction methods. This study presents a machine learning based framework for predicting AMR across multiple bacterial species using genomic sequence data. The approach integrates K-mer analysis, mutation detection, and AMR gene profling to extract key genomic features relevant to resistance classification. The proposed method achieves high predictive accuracy, identifying K-mer signatures, and AMR gene variations as significant contributors. This scalable approach enhances genomic surveillance and clinical decision-making, enabling efficient AMR detection without the need for phenotypic testing.88 pagesenBRAC University theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission.AntimicrobialMutationk-merHotspotClinicalGene proflingMachine learning.Machine learning-based prediction of acquired antimicrobial resistance in multiple bacterial species using K-mer analysis, mutation detection, and AMR gene profilingThesis