Diagnosis of Childhood and Adolescent Growth Hormone Deficiency Using Transcriptomic Data

    February 2023 in “ Frontiers in Endocrinology
    Terence Garner, Ivan Wangsaputra, Andrew Whatmore, Peter Clayton, Adam Stevens, Philip Murray
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    Studysummary This study demonstrates that combining gene expression data with a random forest algorithm provides highly accurate diagnosis of childhood growth hormone deficiency, showing potential utility in distinguishing it from non-GHD short stature.
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    The study demonstrated that childhood growth hormone deficiency (GHD) could be diagnosed with high accuracy by combining gene expression (GE) data with random forest analysis. This approach provided a reliable method for identifying GHD in children and adolescents, potentially improving diagnostic precision compared to traditional methods.
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