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Probabilistic Boolean Networks (PBNs) are rule-based models for gene regulatory networks. Previously, we proposed a method for finding the control policies with the highest effect on steady-state distributions of PBNs. To this end, the theory of infinite-horizon optimal stochastic control was employed. The control variable was chosen to be one of the genes in the model. A natural question that arises is which gene in the network would have the greatest impact on the desired behavior. In principle, solving the optimal control problem for all the candidate genes does answer the question. However, this would be computationally prohibitive. We introduce an algorithm which predicts the best candidate gene. The algorithm suggests a stationary policy for each gene. The best control gene is the one with the highest effect on the stationary distribution once its stationary control policy is applied. The algorithm employs the concept of mean-first-passage-time and has very low complexity.