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Fig. 1 | BMC Systems Biology

Fig. 1

From: High-dimensional omics data analysis using a variable screening protocol with prior knowledge integration (SKI)

Fig. 1

A brief description of (i)SKI procedure. For each variable, two ranks are generated, one based on prior knowledge (R 0), the other based on marginal correlation (R 1). A predefined α, (or estimated based on the dev. ratio) is used to control the weight of prior knowledge. Variables are then sorted by weighted geometric mean of two ranks. SKI first reduces the variable number from p to d, and then a more sophisticated method such as SCAD is used to further refine the model to size d ’ and estimate the parameters. iSKI updates the marginal correlation based rank (R 1) by regressing residues over the rest p − d ’ variables. The procedure is repeated until the desired number of parameters obtained

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