mariadb sql database Search Results


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MariaDB Corporation sql, object-relational database system
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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
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MariaDB Corporation sql-based relational database mariadb version 10.3.32
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 Based Relational Database Mariadb Version 10.3.32, 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
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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).

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