Performance of explaining gene expression in E2 vs. control treated MCF-7 cells using core regulators identified by various ranking strategies.

Three different mathematical or AI models were used for modeling gene expression: linear regression (LR), support vector machines (classification, SVC, and regression, SVR) and principal component analysis (PCA). Performance was measured as area under the ROC curve (AUROC) for real-valued estimators and using Matthew’s correlation coefficient (MCC) for binary classifiers in 5-fold cross validation.