Inference of regulatory networks with MCMC sampler guided by mutual information

Nilzair M. Barreto, K. Machado, A. Werhli
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引用次数: 1

Abstract

Computationally efficient and exact inference of regulatory network topology is an open problem in System Biology. In this work we investigate the use of prior information about the network topology as a guide to a Markov Chain Monte Carlo sampler of network structures. The prior information is obtained from a coarser and faster network inference method, the Relevance Networks with Mutual Information scores. Moreover, the regulatory networks are represented by the Bayesian Networks model. The results show that the use of prior information drastically improves the convergence of the MCMC sampler. Therefore, the use of a more refined method is justified as it is likely to lead to more reliable results with less MCMC iterations.
互信息引导下MCMC采样器调节网络的推理
调节网络拓扑的计算效率和精确推理是系统生物学中的一个开放性问题。在这项工作中,我们研究了使用网络拓扑的先验信息作为网络结构的马尔可夫链蒙特卡罗采样器的指南。先验信息是通过一种更粗糙、更快的网络推理方法获得的,即互信息评分的关联网络。此外,监管网络用贝叶斯网络模型表示。结果表明,先验信息的使用大大提高了MCMC采样器的收敛性。因此,使用更精细的方法是合理的,因为它可能以更少的MCMC迭代产生更可靠的结果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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