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首页> 外文会议>International Congress on Image and Signal Processing, BioMedical Engineering and Informatics >Applying Reversible Jump MCMC to Detect Cancer Associated Genes
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Applying Reversible Jump MCMC to Detect Cancer Associated Genes

机译：应用可逆跃迁MCMC检测癌症相关基因

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摘要

Oncogenes detecting is a very difficult job and a large number of remarkable research accomplishments were made in this area to achieve the goal of identifying reasonable tumor biomarkers. In the paper, a reversible jump MCMC-based algorithm is proposed to discover reasonable tumor-related oncogenes. Firstly, a novel reversing jump-leap Markov Chain Monte Carlo method, called rjMCMC, is presented and designed for Gene Regulatory Network inference. Secondly, with the rjMCMC, the novelty networks can be built from real-world and man-made gene expression data. In the method, Bayesian posterior inference is utilized and exploited to evaluate and calculate values of the network pattern or parameters, and MCMC is employed to perform search task. Thirdly, by introducing a reversible jump and posterior inference method, we can easily estimate or discover the patterns of oncogenes of the differential network and then discover cancer genes. Compared with GlobalMIT, TSNI and NIR, the rjMCMC technique eminently enhances the precision and correctness on identification of complex disease related-genes.
机译：癌基因的检测是一项非常艰巨的工作，在这一领域已经取得了许多显着的研究成果，以实现鉴定合理的肿瘤生物标记物的目的。本文提出了一种基于MCMC的可逆跳算法，以发现合理的肿瘤相关致癌基因。首先，提出了一种新颖的反向跳跃跳跃马尔可夫链蒙特卡罗方法，称为rjMCMC，并设计用于基因调控网络推断。其次，利用rjMCMC，可以从现实世界和人造基因表达数据构建新颖性网络。在该方法中，利用和利用贝叶斯后验来评估和计算网络模式或参数的值，并使用MCMC来执行搜索任务。第三，通过引入可逆跳跃和后验推断方法，我们可以轻松地估计或发现差异网络癌基因的模式，然后发现癌症基因。与GlobalMIT，TSNI和NIR相比，rjMCMC技术显着提高了识别复杂疾病相关基因的准确性和正确性。

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