About this Research Topic
Researchers can contribute to best practices for using WGS-based analytics to inform surveillance systems, for example, by identifying emerging pathogen subtypes, discovering novel biomarkers of pathogenicity and zoonotic potential, and monitoring for antimicrobial resistance. We welcome studies along these lines that integrate genomic analyses with food inspection systems. These studies would help guide food and environmental sampling, metadata collection, and the integration of supplementary (non-genomic) data like production records, use of safety interventions, and weather data, with genomic data to promote food safety.
We particularly seek studies that improve or add to existing quantitative methods for use of WGS data for food inspection, principally those that aid objective bio-marker discovery for use with predictive analytics. In contrast to the use of quantitative methods with human data, analysis of microbial data may require adjustments for non-linearity due to within-host genetic diversity and gene dosage effects. Lastly, we seek studies that guide the design of new inspection systems that could use WGS data, including sampling plans and techniques and methods for detecting pathogen subtypes to avoid surveillance bias.
Keywords: Genome wide association study (GWAS), Supervised Machine Learning, Unsupervised Machine Learning, predictive analytics, surveillance bias, biomarker discovery, pathogenicity, zoonotic potential, virulence, antimicrobial resistance
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