Markov chain Monte Carlo for active module identification problem.
Nikita AlexeevJavlon IsomurodovVladimir SukhovGennady KorotkevichAlexey SergushichevPublished in: BMC bioinformatics (2020)
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.