Harmonization of supervised machine learning practices for efficient source attribution of Listeria monocytogenes based on genomic data.

ABSTRACT:

Background

Genomic data-based machine learning tools are promising for real-time surveillance activities performing source attribution of foodborne bacteria such as Listeria monocytogenes. Given the heterogeneity of machine learning practices, our aim was to identify those influencing the source prediction performance of the usual holdout method combined with the repeated k-fold cross-validation method.

Methods

A large collection of 1 100 L. monocytogenes genomes with known sources was built according to several genomic metrics to ensure authenticity and completeness of genomic profiles. Based on these genomic profiles (i.e. 7-locus alleles, core alleles, accessory genes, core SNPs and pan kmers), we developed a versatile workflow assessing prediction performance of differen

SUBMITTER: Castelli P 

PROVIDER: S-EPMC10515079 | biostudies-literature | 2023 Sep

REPOSITORIES: biostudies-literature