Applying Reversible Jump MCMC to Detect Cancer Associated Genes

摘要: Oncogenes detecting is a very difficult job and large number of remarkable research accomplishments were made in this area to achieve the goal identifying reasonable tumor biomarkers. In paper, reversible jump MCMC-based algorithm proposed discover tumor-related oncogenes. Firstly, novel reversing jump-leap Markov Chain Monte Carlo method, called rjMCMC, presented designed for Gene Regulatory Network inference. Secondly, with novelty networks can be built from real-world man-made gene expression data. Bayesian posterior inference utilized exploited evaluate calculate values network pattern or parameters, MCMC employed perform search task. Thirdly, by introducing we easily estimate patterns oncogenes differential then cancer genes. Compared GlobalMIT, TSNI NIR, rjMCMC technique eminently enhances precision correctness on identification complex disease related-genes.