Markov chain Monte Carlo for active module identification problem.

The proposed method allows to estimate the probability that an individual vertex belongs to the active module as well as the false discovery rate (FDR) for a given set of vertices. Given the estimated probabilities, it becomes possible to provide a connected subgraph in a consistent manner for any given FDR level: no vertex can disappear when the FDR level is relaxed. We show, on both simulated and real datasets, that the proposed method has good computational performance and high classification accuracy.