Degree
Doctor of Philosophy (PhD)
Department
Environmental Science
Document Type
Dissertation
Abstract
Antibiotic resistance represents an escalating global health challenge driven by the overuse of antibiotics and the environmental dissemination of antibiotic-resistant bacteria and genes. Rivers act as key conduits and reservoirs for resistance dissemination, receiving continual inputs from wastewater effluents, agricultural runoff, and urban sources. This dissertation integrates a review of ARG detection methods with the development of artificial intelligence-based models for improved identification and classification of resistance genes, followed by an application to the Mississippi River.
Three machine learning frameworks, Trans-ARG, GNN-ARG, and ESM-ARG, were developed using transformer networks, graph neural architectures, and protein language model embeddings to capture structural and functional information within protein sequences. The ESM-ARG model, leveraging embeddings from Meta’s ESM-2, achieved higher predictive accuracy than existing models such as DeepARG, ARGNet, and ARG-SHINE. The framework was applied to metagenomic assemblies from the Mississippi River to assess the riverine resistome. Fewer than 1 percent of open reading frames (ORFs) were classified as ARGs, dominated by beta-lactam, aminoglycoside, multidrug and tetracycline resistance classes. Collectively, this research demonstrates that protein embedding-based deep learning substantially enhances ARG prediction and provides scalable, alignment-free tools for environmental resistome monitoring in complex aquatic ecosystems.
Date
11-3-2025
Recommended Citation
Abbas, Mohd Manzar, "DEVELOPING AI MODELS FOR ANTIBIOTIC RESISTANCE GENE PREDICTION AND A CASE STUDY IN THE MISSISSIPPI RIVER" (2025). LSU Doctoral Dissertations. 6954.
https://repository.lsu.edu/gradschool_dissertations/6954
Committee Chair
Hou, Aixin