Reconstructing Causal Biological Networks through Active Learning.
Reconstructing Causal Biological Networks through Active Learning.
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DOI：
10.1371/journal.pone.0150611
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发表时间：
2016
期刊：
影响因子：
3.7
通讯作者：
Peng J
中科院分区：
文献类型：
--
作者：
Cho H;Berger B;Peng J
生物网络的逆向工程是系统生物学中的一个核心问题。使用干预数据，如基因敲除或敲除，通常用于梳理基因之间的因果关系。在时间或资源有限的情况下，需要仔细选择进行哪些干预实验。以前选择信息量最大的干预措施的方法主要集中在离散贝叶斯网络上。然而，连续贝叶斯网络有很大的实际意义，特别是在复杂生物系统及其定量性质的研究中。在这项工作中，我们提出了一种有效的信息论主动学习算法，用于高斯贝叶斯网络（GBNs），这是基因调控网络的重要模型。除了提供gbn独有的线性代数见解，从而显著改善运行时间外，我们还证明了我们的方法在gbn和DREAM4网络推理挑战数据集模拟数据上的有效性。与随机选择干预实验相比，我们的方法通常可以更快地恢复底层网络结构，并更快地收敛到使用完整数据的候选图结构上的置信度得分的最终分布。
Reverse-engineering of biological networks is a central problem in systems biology. The use of intervention data, such as gene knockouts or knockdowns, is typically used for teasing apart causal relationships among genes. Under time or resource constraints, one needs to carefully choose which intervention experiments to carry out. Previous approaches for selecting most informative interventions have largely been focused on discrete Bayesian networks. However, continuous Bayesian networks are of great practical interest, especially in the study of complex biological systems and their quantitative properties. In this work, we present an efficient, information-theoretic active learning algorithm for Gaussian Bayesian networks (GBNs), which serve as important models for gene regulatory networks. In addition to providing linear-algebraic insights unique to GBNs, leading to significant runtime improvements, we demonstrate the effectiveness of our method on data simulated with GBNs and the DREAM4 network inference challenge data sets. Our method generally leads to faster recovery of underlying network structure and faster convergence to final distribution of confidence scores over candidate graph structures using the full data, in comparison to random selection of intervention experiments.