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cgbayesnets  (MathWorks Inc)


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    MathWorks Inc cgbayesnets
    Data-driven and knowledge-based tools for computational analysis of microbial metabolomics data that were developed within the past two years.
    Cgbayesnets, supplied by MathWorks Inc, 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/result/cgbayesnets/product/MathWorks Inc
    Average 90 stars, based on 1 article reviews
    cgbayesnets - by Bioz Stars, 2026-03
    90/100 stars

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    1) Product Images from "Recent advances in data- and knowledge-driven approaches to explore primary microbial metabolism"

    Article Title: Recent advances in data- and knowledge-driven approaches to explore primary microbial metabolism

    Journal: Current Opinion in Chemical Biology

    doi: 10.1016/j.cbpa.2023.102324

    Data-driven and knowledge-based tools for computational analysis of microbial metabolomics data that were developed within the past two years.
    Figure Legend Snippet: Data-driven and knowledge-based tools for computational analysis of microbial metabolomics data that were developed within the past two years.

    Techniques Used: Extraction, Generated, Amplification, Sequencing, Biomarker Discovery, Produced, Construct



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    Image Search Results


    Data-driven and knowledge-based tools for computational analysis of microbial metabolomics data that were developed within the past two years.

    Journal: Current Opinion in Chemical Biology

    Article Title: Recent advances in data- and knowledge-driven approaches to explore primary microbial metabolism

    doi: 10.1016/j.cbpa.2023.102324

    Figure Lengend Snippet: Data-driven and knowledge-based tools for computational analysis of microbial metabolomics data that were developed within the past two years.

    Article Snippet: CGBayesNets (MATLAB) [ ] , Analysis of temporal variation of the gut microbiota , Microbiota abundance values , Sample phenotypes , Conditional Gaussian Bayesian networks that predict sample phenotypes (e.g. patient status) and provide information on feature importance for interpretation (e.g. biomarker discovery)..

    Techniques: Extraction, Generated, Amplification, Sequencing, Biomarker Discovery, Produced, Construct