We developed a procedure for indentifying transcriptional master regulators (MRs) related to special biological phenomena, such as diseases, in conjunction with network screening and inference. Network screening is a system for detecting activated transcriptional regulatory networks under particular conditions, based on the estimation of the graph structure consistency with the measured data. Since the network screening utilizes the known transcriptional factor (TF)-gene relationships as the experimental evidence for the molecular relationships, its performance depends on the ensemble of known TF networks used for its analysis. To compensate for its restrictions, a network inference method, the path consistency algorithm, is concomitantly utilized to identify MRs. The performance is illustrated by means of the known MRs in brain tumors that were computationally inferred and experimentally verified. As a result, the present procedure worked well for identifying MRs, in comparison to the previous computational selection for experimental verification.