Food safety is of international concern, yet foodborne illnesses continue to occur caused by a variety of microorganisms, including viruses (such as norovirus and hepatitis A), pathogenic bacteria (such as Salmonella and E. coli) and mycotoxin producing fungi. With the development of sequencing, bioinformatics and big data technologies, the use of computational technologies open a new window in the field of food safety.
To date, new strategies driven by bioinformatics, machine learning and risk analysis improve the detection, diagnosis, prediction and prevention of foodborne disease outbreak, which provide recommendations for reducing and minimizing microbial food safety hazards. For example, whole genome sequencing can identify pathogens within days, allowing for timely surveillance and recall of contaminated food and effective diagnosis and treatment of patients. Hence, this poses a huge challenge for integration, utilization and analyzing of all the components involved.
This Research Topic will provide an overview of the current status and development trends of bioinformatics, machine learning and risk analysis applications in food safety. Including research on foodborne pathogens and epidemic prevention and control, as well as discussion on the advantages, disadvantages and future prospects of the applications in food safety. We invite authors to contribute Original Research articles or Reviews for the open problems in these areas.
Food safety is of international concern, yet foodborne illnesses continue to occur caused by a variety of microorganisms, including viruses (such as norovirus and hepatitis A), pathogenic bacteria (such as Salmonella and E. coli) and mycotoxin producing fungi. With the development of sequencing, bioinformatics and big data technologies, the use of computational technologies open a new window in the field of food safety.
To date, new strategies driven by bioinformatics, machine learning and risk analysis improve the detection, diagnosis, prediction and prevention of foodborne disease outbreak, which provide recommendations for reducing and minimizing microbial food safety hazards. For example, whole genome sequencing can identify pathogens within days, allowing for timely surveillance and recall of contaminated food and effective diagnosis and treatment of patients. Hence, this poses a huge challenge for integration, utilization and analyzing of all the components involved.
This Research Topic will provide an overview of the current status and development trends of bioinformatics, machine learning and risk analysis applications in food safety. Including research on foodborne pathogens and epidemic prevention and control, as well as discussion on the advantages, disadvantages and future prospects of the applications in food safety. We invite authors to contribute Original Research articles or Reviews for the open problems in these areas.
