Machine learning approaches to predict the Plant-associated phenotype of Xanthomonas strains.

ABSTRACT:

Background

The genus Xanthomonas has long been considered to consist predominantly of plant pathogens, but over the last decade there has been an increasing number of reports on non-pathogenic and endophytic members. As Xanthomonas species are prevalent pathogens on a wide variety of important crops around the world, there is a need to distinguish between these plant-associated phenotypes. To date a large number of Xanthomonas genomes have been sequenced, which enables the application of machine learning (ML) approaches on the genome content to predict this phenotype. Until now such approaches to the pathogenomics of Xanthomonas strains have been hampered by the fragmentation of information regarding pathogenicity of individual strains over many studies. Unification of this informa

SUBMITTER: Te Molder D 

PROVIDER: S-EPMC8612006 | biostudies-literature | 2021 Nov

REPOSITORIES: biostudies-literature