基于消息传递的基因网络推理
Gene-network inference by message passing
摘要：
从基因表达数据推断基因调控过程属于计算系统生物学的主要挑战。在这里，我们解决这个问题，从物理学的角度来看，并开发了一个消息传递算法，能够推断稀疏，定向和组合的监管机制。使用副本技术，算法的性能可以分析为人工生成的数据。该算法适用于面包酵母在各种环境条件下的全基因组表达数据。我们发现明确的情况下，组合控制，并在共同的功能注释丰富的调节基因及其监管机构。
英文摘要：
The inference of gene-regulatory processes from gene-expression data belongs to the major challenges of computational systems biology. Here we address the problem from a statistical-physics perspective and develop a message-passing algorithm which is able to infer sparse, directed and combinatorial regulatory mechanisms. Using the replica technique, the algorithmic performance can be characterized analytically for artificially generated data. The algorithm is applied to genome-wide expression data of baker's yeast under various environmental conditions. We find clear cases of combinatorial control, and enrichment in common functional annotations of regulated genes and their regulators.
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