Fig. 1From: Epistasis analysis of microRNAs on pathological stages in colon cancer based on anĀ Empirical Bayesian Elastic Net methodAn overview of the EBEN algorithm.Ā 1) Initialize model parameters and the statistical model. The unknown parameters Ī¼ 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 Ī± 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 Ī² ā² that denotes the significant results and the covariance matrixBack to article page