Machine Learning Methods as a Tool for Predicting Risk of Illness Applying Next-Generation Sequencing Data.
Next-generation sequencing (NGS) data present an untapped potential to improve microbial risk assessment (MRA) through increased specificity and redefinition of the hazard. Most of the MRA models do not account for differences in survivability and virulence among strains. The potential of machine learning algorithms for predicting the risk/health burden at the population level while inputting large and complex NGS data was explored with Listeria monocytogenes as a case study. Listeria data consisted of a percentage similarity matrix from genome assemblies of 38 and 207 strains of clinical and food origin, respectively. Basic Local Alignment (BLAST) was used to align the assemblies against a database of 136 virulence and stress resistance genes. The outcome variable was frequency of illness, which is the percentage of reported cases associated with each strain. These frequency data were discretized into seven ordinal outcome categories and used for supervised machine learning and model selection from five ensemble algorithms. There was no significant difference in accuracy between the models, and support vector machine with linear kernel was chosen for further inference (accuracy of 89% [95% CI: 68%, 97%]). The virulence genes FAM002725, FAM002728, FAM002729, InlF, InlJ, Inlk, IisY, IisD, IisX, IisH, IisB, lmo2026, and FAM003296 were important predictors of higher frequency of illness. InlF was uniquely truncated in the sequence type 121 strains. Most important risk predictor genes occurred at highest prevalence among strains from ready-to-eat, dairy, and composite foods. We foresee that the findings and approaches described offer the potential for rethinking the current approaches in MRA.
['Njage|Patrick Murigu Kamau|PMK|', 'Henri|Clementine|C|', 'Leekitcharoenphon|Pimlapas|P|', 'Mistou|Michel-Yves|MY|', 'Hendriksen|Rene S|RS|', 'Hald|Tine|T|']
D000465:Algorithms D030541:Databases, Genetic D005502:Food D005516:Food Microbiology D005517:Foodborne Diseases D014644:Genetic Variation D059014:High-Throughput Nucleotide Sequencing D006801:Humans D016014:Linear Models D008089:Listeria monocytogenes D008088:Listeriosis D000069550:Machine Learning D010641:Phenotype D011336:Probability D018570:Risk Assessment D012680:Sensitivity and Specificity D014774:Virulence
machine learning microbial risk assessment Listeria monocytogenes support vector machines whole genome sequencing