基因网络推理算法的统计力学分析
Inference algorithms for gene networks: a statistical mechanics analysis
摘要：
从高通量的基因表达数据推断基因调控网络是系统生物学的主要挑战之一。本文旨在分析和比较两种不同的算法方法。第一种方法使用成对的相关性之间的调节和调节基因，第二个使用信息传递技术来推断激活和抑制监管的相互作用。这两种算法的性能可以在定义良好的测试集上进行理论分析，使用来自无序系统统计物理学的工具，如副本方法。我们发现，第二个算法优于第一个，因为它考虑到多个监管机构的集体效应。
英文摘要：
The inference of gene regulatory networks from high throughput gene expression data is one of the major challenges in systems biology. This paper aims at analysing and comparing two different algorithmic approaches. The first approach uses pairwise correlations between regulated and regulating genes; the second one uses message-passing techniques for inferring activating and inhibiting regulatory interactions. The performance of these two algorithms can be analysed theoretically on well-defined test sets, using tools from the statistical physics of disordered systems like the replica method. We find that the second algorithm outperforms the first one since it takes into account collective effects of multiple regulators.
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