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  "Description": "PaIRKAT is model framework for assessing statistical\nrelationships between networks of metabolites (pathways) and an\noutcome of interest (phenotype). PaIRKAT queries the KEGG\ndatabase to determine interactions between metabolites from\nwhich network connectivity is constructed. This model framework\nimproves testing power on high dimensional data by including\ngraph topography in the kernel machine regression setting.\nStudies on high dimensional data can struggle to include the\ncomplex relationships between variables. The semi-parametric\nkernel machine regression model is a powerful tool for\ncapturing these types of relationships. They provide a\nframework for testing for relationships between outcomes of\ninterest and high dimensional data such as metabolomic,\ngenomic, or proteomic pathways. PaIRKAT uses known biological\nconnections between high dimensional variables by representing\nthem as edges of ‘graphs’ or ‘networks.’ It is common for nodes\n(e.g. metabolites) to be disconnected from all others within\nthe graph, which leads to meaningful decreases in testing power\nwhether or not the graph information is included. We include a\ngraph regularization or ‘smoothing’ approach for managing this\nissue.",
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  "Encoding": "UTF-8",
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