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Workflow. Xanthomonas pathogenicity assay data for different strains was obtained from literature and stored in a SQL (phenotype) database. Available genomes were retrieved and de novo annotated with protein domains. Annotation results were stored in a Graph database. Strain specific domain content was used as input to train the classifiers. Resulting models were examined for their ability to predict pathogenicity and feature importance

Journal: BMC Genomics

Article Title: Machine learning approaches to predict the Plant-associated phenotype of Xanthomonas strains

doi: 10.1186/s12864-021-08093-0

Figure Lengend Snippet: Workflow. Xanthomonas pathogenicity assay data for different strains was obtained from literature and stored in a SQL (phenotype) database. Available genomes were retrieved and de novo annotated with protein domains. Annotation results were stored in a Graph database. Strain specific domain content was used as input to train the classifiers. Resulting models were examined for their ability to predict pathogenicity and feature importance

Article Snippet: Relevant parameters and outcomes were stored in a SQL database ( MariaDB v10.5.9).

Techniques: