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Research Topic : BIOINFORMATICS
Field of Research : Systems Biology
Australian State/Territory : NSW
Status : Closed
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  • Researchers (18)
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  • Funded Activity

    Discovery Projects - Grant ID: DP110102680

    Funder
    Australian Research Council
    Funding Amount
    $300,000.00
    Summary
    Commensal benefits: genomic basis for suppressing plant pathogens with Pseudomonas biocontrol species. Food security is an issue of mounting significance due to unpredictable climate trends and increasing global population growth. A feature of paramount importance to reliable crop production is the capacity to control plant diseases. This project investigates natural plant colonising bacteria as a tool for protecting plants from disease.
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    Funded Activity

    Linkage Infrastructure, Equipment And Facilities - Grant ID: LE130100007

    Funder
    Australian Research Council
    Funding Amount
    $650,000.00
    Summary
    A research platform for exploring the genotype: phenotype nexus. This project will allow us to connect the genetic code of an organism with its characteristic traits that are essential for its survival. The equipment will accelerate research that performs this translation, and will allow leading Australian scientists to continue to make breakthroughs in this field globally.
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    Funded Activity

    Discovery Early Career Researcher Award - Grant ID: DE170100759

    Funder
    Australian Research Council
    Funding Amount
    $372,000.00
    Summary
    Trans-omic networks: A machine learning and omics integration approach. This project aims to map and model ‘trans-omic’ networks that cut through omic layers using machine learning and multi-omic data integration. Global networks regulated by molecular programs, including signalling, epigenetic, transcriptional and translational regulation, orchestrate cellular functions. Technological advances can profile these molecular programmes, giving rise to various ‘omics’. However, data generated from e .... Trans-omic networks: A machine learning and omics integration approach. This project aims to map and model ‘trans-omic’ networks that cut through omic layers using machine learning and multi-omic data integration. Global networks regulated by molecular programs, including signalling, epigenetic, transcriptional and translational regulation, orchestrate cellular functions. Technological advances can profile these molecular programmes, giving rise to various ‘omics’. However, data generated from each omic layer are predominantly analysed separately owing to their heterogeneity. To understand cellular functions in its entirety, it is essential to interpret omic data across multiple omic layers. Applying this project’s methods is expected to improve use of omics data and fundamental molecular programs.
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