Machine Learning Methods as a Tool for Predicting Risk of Illness Applying Next ‐Generation Sequencing Data

AbstractNext ‐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 lear ning algorithms for predicting the risk/health burden at the population level while inputting large and complex NGS data was explored withListeria 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 genesFAM002725, FAM002728, FAM002729, InlF,InlJ,Inlk, IisY,IisD,IisX,IisH,IisB,lmo2026, andFAM003296 were important predictors of higher frequency of illness.InlF was uniquely truncated in the sequence type 121 strains. Most important risk predict...
Source: Risk Analysis - Category: International Medicine & Public Health Authors: Tags: Original Research Article Source Type: research