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mariadb sql database  (MariaDB Corporation)

 
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    MariaDB Corporation mariadb sql database
    Mariadb Sql Database, 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/pmc11124797-233-4-4
    Average 90 stars, based on 1 article reviews
    mariadb sql database - by Bioz Stars, 2026-09
    90/100 stars

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    other:

    Article Title: SINBAD, structural, experimental and clinical characterization of STAT inhibitors and their potential applications
    Article Snippet: For the database creation we used Django web framework, Docker for efficient deployment, Nginx as web server, Elasticsearch as a search engine and finally MariaDB as SQL database.

    Article Title: A web-based platform for the annotation and analysis of NAR-published databases.
    Article Snippet: Data storage and retrieval for the website use a standard MariaDB SQL database.

    Article Title: Smart City as Cooperating Smart Areas: On the Way of Symbiotic Cyber–Physical Systems Environment
    Article Snippet: The FM uses the MariaDB SQL database as a relational database management system and InfluxDB connected to Grafana in order to manage time-series data with a multitude of advantages: single datastore for all time-series data, low-latency queries, open and interoperable with data ecosystems, high data compression.

    Article Title: Dragonfly Hunter CZ: Mobile application for biological species recognition in citizen science
    Article Snippet: The server side is powered by Apache Server with PHP and a MariaDB SQL database.

    Article Title: Bac Dive in 2022: the knowledge base for standardized bacterial and archaeal data
    Article Snippet: The Graphical User Interface (GUI) of Bac Dive is built with an individually developed PHP front end, combined with 108 tables stored in a MariaDB SQL database.




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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: