Figure 7

Performance gain using integrative modeling. The expected AUC(ROC) of a random prediction is normally distributed around 0.5 as calculated by 1,000 repeated runs of RAND. The gene-TF-RAND algorithm considers the GRN template information, but assigns gene-TF interactions randomly. In contrast, the TILAR algorithm utilizes gene expression data to infer gene-TF interactions, while only a subset of the predicted TF-gene interactions is included into the final model. This significantly increases the inference quality. However, the method could be further improved by considering text mining information on which genes possibly regulate TF activity (adaptive TILAR). The combined inference method allows to strike a balance between data-fit and confidence in such putative gene-TF interactions by means of the parameters in δ.