id	author	title	date	pages	extension	mime	words	sentence	flesch	summary	cache	txt
ajb-262	Bradford, Dreshawn; Lodhi, Khalid; Yuan, Jiazheng; Graham, Danielle; Graham, Justin; Maldani, Mohamed; White, Erin; Arhin, Afua; Kassem, My Abdelmajid 	Genomic Diversity, Pathogenicity, and Microbial Forensics of Foodborne Bacteria: A Comparative Analysis	2025	10	.pdf	application/pdf	7200	466	50	Machine learning and AI approaches are increasingly being used to analyze these complex genomic and ecological patterns, enhancing our under- standing of pathogen evolution and spread (Jiang et al., 2022; Danko et al., 2021; Libbrecht and Noble, 2015). Future research should explore machine learning approaches to integrate genomic, epidemiological, and clinical data for more accurate pathogenicity risk assessments (Jiang et al., 2022; Libbrecht and Noble, 2015).	cache/ajb-262.pdf	txt/ajb-262.txt