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Fig. 1 | BMC Genomics

Fig. 1

From: Epistasis analysis of microRNAs on pathological stages in colon cancer based on an Empirical Bayesian Elastic Net method

Fig. 1

An overview of the EBEN algorithm. 1) Initialize model parameters and the statistical model. The unknown parameters μ denotes the mean of phenotype, \( \overset{\sim }{y} \) denotes the initial dependent variable and \( {\sigma}_0^2 \) denotes the variance of the model, obtain the initial features satisfying \( k={arg}_i\left\{\left|{x}_i^T\overset{\sim }{y}\right|,\forall i\right\} \). Here, k denotes the subscripts of features, x i denotes the vector of feature i, \( \overset{\sim }{y} \) denotes the dependent variable in the statistical model, and α k is a variable calculated from \( {\sigma}_k^2 \), 2) Update the parameters in the model during iterations, 3) Use t-test to perform hypothesis test on the estimated value, and 4) Output β that denotes the significant results and the covariance matrix

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