|PENG, YUN - University Of Maryland|
|MENG, JINGHONG - University Of Maryland|
|RUSANTE, JULIANA - University Of Maryland|
Submitted to: Software Tools and Algorithms for Biological Systems
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 4/1/2010
Publication Date: 4/1/2011
Citation: Yan, X., Peng, Y., Meng, J., Rusante, J., Fratamico, P.M., Huang, L., Juneja, V.K., Needleman, D.S. 2011. From ontology selection and semantic web to the integrated information system of food-borne diseases and food safety. Software Tools and Algorithms for Biological Systems. 26-34.
Interpretive Summary: Established and emerging food-borne pathogens continue to challenge public health, and there are no coordinated efforts or centralized information systems to handle the complex, isolated, and semantically heterogeneous data resources on food-borne pathogens, disesases, and outbreaks. This paper intends to discuss how to adapt and develop advanced information and communication technologies to provide a computational model in semantic web for greater adaptability, robustness, and richness of food-borne pathogen information for the public in a timely manner. This infrastructure will allow the systematic integration of food-borne pathogen and food safety information from different databases maintained worldwide, of data on pathogen profiling and predictive models of growth and inactivation of pathogens during food processing operations, and on epidemiologic and regulatory data. The ultimate purpose of this paper is to communicate the framework for this centralized information system to the scientific community to facilitate further discussion on how to allow rapid collection, analysis, and sharing of data, which can assist in outbreak investigations, risk management, and pathogen surveillance and control.
Technical Abstract: Over the last three decades, the rapid explosion of information and resources on human food-borne diseases and food safety has provided the ability to rapidly determine and interpret the mechanisms of survival and pathogenesis of food-borne pathogens. However, several factors have hindered effective use of the information and resources due to inconsistency among semantically heterogeneous data resources, lack of knowledge on profiling of food-borne pathogens, and knowledge gaps among research communities, government risk assessors/managers, and end users of the information. The systematic integration of heterogeneous data resources, microbial pathogen profiling, risk analyses, and other information related to environmental factors that affect pathogen virulence will significantly enhance foodborne illness prevention, and surveillance programs, and improve risk management strategies for the food industry and regulatory agencies (e.g. World Health Organization, USDA Food Safety and Inspection Service, Centers for Disease Control and Prevention, and the Food and Drug Administration). In this paper, we will discuss technical aspects of the rationale to: a) computationally collect and compile publicly available information, including published microbial pathogen profiling data relevant to genomic, proteomic, and metabolomic analyses; b) develop ontology libraries on food-borne pathogens and design automatic algorithms with formal inference and fuzzy and probabilistic reasoning to address the consistency and accuracy of distributed information resources (e.g., PulseNet, FoodNet, OutbreakNet, PubMed, NCBI, EMBL, and online genetic databases and information); c) integrate newly collected pathogen profiling data, Foodrisk.org (http://www.foodrisk.org), PMP, Combase, and other relevant information into a user-friendly “homogeneous” information system available to scientists in academia, the food industry, and government agencies and d) develop a computational model in semantic web for greater adaptability and robustness, thereby, providing a richer learning environment. The purpose of this paper is to discuss how a comprehensive food safety information reporting system could serve as an efficient early warning system in outbreak investigations and can be used for risk management and food-borne pathogen surveillance and control.