On the impact of entropy estimation on transcriptional regulatory network inference based on mutual information.

Catharina Olsen, Patrick E Meyer, Gianluca Bontempi
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引用次数: 73

Abstract

The reverse engineering of transcription regulatory networks from expression data is gaining large interest in the bioinformatics community. An important family of inference techniques is represented by algorithms based on information theoretic measures which rely on the computation of pairwise mutual information. This paper aims to study the impact of the entropy estimator on the quality of the inferred networks. This is done by means of a comprehensive study which takes into consideration three state-of-the-art mutual information algorithms: ARACNE, CLR, and MRNET. Two different setups are considered in this work. The first one considers a set of 12 synthetically generated datasets to compare 8 different entropy estimators and three network inference algorithms. The two methods emerging as the most accurate ones from the first set of experiments are the MRNET method combined with the newly applied Spearman correlation and the CLR method combined with the Pearson correlation. The validation of these two techniques is then carried out on a set of 10 public domain microarray datasets measuring the transcriptional regulatory activity in the yeast organism.

Abstract Image

Abstract Image

熵估计对基于互信息的转录调控网络推断的影响。
从表达数据的转录调控网络的逆向工程在生物信息学社区中获得了很大的兴趣。基于信息论测度的算法是一类重要的推理技术,它依赖于两两互信息的计算。本文旨在研究熵估计量对推断网络质量的影响。这是通过一项综合研究来完成的,该研究考虑了三种最先进的互信息算法:ARACNE, CLR和MRNET。在这项工作中考虑了两种不同的设置。第一种方法考虑了一组12个综合生成的数据集,比较了8种不同的熵估计器和3种网络推理算法。第一组实验中出现的最准确的两种方法是结合新应用的Spearman相关的MRNET方法和结合Pearson相关的CLR方法。这两种技术的验证随后在一组10个公共领域微阵列数据集上进行,测量酵母生物体内的转录调控活性。
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