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sql-based relational database mariadb version 10.3.32  (MariaDB Corporation)

 
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    MariaDB Corporation sql-based relational database mariadb version 10.3.32
    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
    https://www.bioz.com/product/mariadb+sql+database/sql+based+relational+database+mariadb+version+10+3+32/pm38422352-76-4-7
    Average 90 stars, based on 1 article reviews
    sql-based relational database mariadb version 10.3.32 - by Bioz Stars, 2026-09
    90/100 stars

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    Article Title: ASACO: Automatic and Serial Analysis of CO-expression to discover gene modifiers with potential use in drug repurposing.
    Article Snippet: Massive gene expression analyses are widely used to find differentially expressed genes under specific conditions.. The results of these experiments are often available in public databases that are undergoing a growth similar to that of molecular sequence databases in the past.. This now allows novel secondary computational tools to emerge that use such information to gain new knowledge.



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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: