Showing 4 open source projects for "disease"

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    PANDORA

    PANDORA

    Revolutionizing Biomedical Research with Advanced Machine Learning

    PANDORA is a machine learning (ML) tool that can be used to integrate various data types, including clinical, transcriptome and microbiome data and find connections in large datasets. PANDORA can be easily installed using Docker, a pre-built version of the software can be pulled from DockerHub. In order to run a test instance of PANDORA, users will first need to prepare their local environment by downloading, installing, and configuring Docker. genular is a community behind SIMON an...
    Downloads: 1 This Week
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  • 2
    scArches

    scArches

    Reference mapping for single-cell genomics

    Single-cell architecture surgery (scArches) is a package for reference-based analysis of single-cell data. scArches allows your single-cell query data to be analyzed by integrating it into a reference atlas. By mapping your data into an integrated reference you can transfer cell-type annotation from reference to query, identify disease states by mapping to healthy atlas, and advanced applications such as imputing missing data modalities or spatial locations.
    Downloads: 0 This Week
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  • 3
    Hetionet

    Hetionet

    Hetionet: an integrative network of disease

    Hetionet is a hetnet — network with multiple node and edge (relationship) types — which encodes biology. The hetnet was designed for Project Rephetio, which aims to systematically identify why drugs work and predict new therapies for drugs. The JSON and Neo4j formats contain node and edge properties, which are absent in the TSV and matrix formats, including licensing information. Therefore the recommended formats are JSON and Neo4j. Our hetio package in Python reads the JSON format, but it...
    Downloads: 0 This Week
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  • 4
    This disease-centric project contributes data integration and analysis tools from the Institute for Systems Biology (ISB). We offer this project to the research community to further our efforts in disease prediction and prevention.
    Downloads: 0 This Week
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