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sql database mariadb v10.5.9  (MariaDB Corporation)

 
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    MariaDB Corporation sql database mariadb v10.5.9
    Workflow. Xanthomonas pathogenicity assay data for different strains was obtained from literature and stored in a <t>SQL</t> (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
    Sql Database Mariadb V10.5.9, supplied by MariaDB Corporation, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/product/mariadb+sql+database/sql+database+mariadb+v10+5+9/pmc08612006-296-8-11
    Average 90 stars, based on 1 article reviews
    sql database mariadb v10.5.9 - by Bioz Stars, 2026-09
    90/100 stars

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    1) Product Images from "Machine learning approaches to predict the Plant-associated phenotype of Xanthomonas strains"

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

    Journal: BMC Genomics

    doi: 10.1186/s12864-021-08093-0

    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
    Figure Legend 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

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    Related Articles

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    Article Title: Machine learning approaches to predict the Plant-associated phenotype of Xanthomonas strains
    Article Snippet: Relevant parameters and outcomes were stored in a SQL database ( MariaDB v10.5.9).



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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: