A Short Review on Harnessing Bioinformatics for Food Safety: Computational Approaches to Detecting Foodborne Pathogens - Syaiful Khoiri; Victor Davy Moussango (Peneliti Teknologi Teknik Indonesia, 2024)
Foodborne diseases remain a significant global public health concern, affecting millions annually and causing substantial economic losses. Traditional microbiological methods for pathogen detection, such as culture-based identification and polymerase chain reaction, are often time-consuming and lack sensitivity. The integration of bioinformatics and high-throughput sequencing technologies, including next-generation sequencing and metagenomics, has revolutionized foodborne pathogen detection by enabling rapid, accurate, and culture-independent identification. Machine learning and artificial intelligence further enhance food safety monitoring through predictive modeling and risk assessment, facilitating early outbreak detection and improved contamination control. Whole genome sequencing has emerged as a gold standard for public health surveillance, allowing for precise pathogen characterization and antimicrobial resistance tracking. Data-sharing networks, such as GenomeTrakr and PulseNet, have strengthened global collaboration, enhancing real-time pathogen monitoring. However, challenges persist in data integration, technical expertise, and infrastructure development, which hinder the widespread adoption of these technologies. Addressing these barriers requires standardized protocols, AI-driven predictive models, and interdisciplinary collaboration between public health, industry, and academia. As bioinformatics continues to evolve, its role in pathogen surveillance, outbreak prevention, and food safety management will become increasingly vital. Advancements in bioinformatics tools and AI-driven approaches will ensure a more efficient, data-driven, and globally coordinated response to foodborne disease threats