Phenotypically constrained Boolean network inference with prescribed steady states

Xiaoning Qian, E. Dougherty
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引用次数: 2

Abstract

In this paper, we investigate a phenotypically constrained inference algorithm to reconstruct genetic regulatory networks modeled as Boolean networks (BNs). Based on a previous universal Minimum Description Length (uMDL) network inference algorithm, we study whether adding the prior information based on prescribed attractors or steady states can help better reconstruct the underlying gene regulatory relationships. Comparing the network inference performance with and without prescribed steady states, the experiments based on randomly generated networks as well as a metastatic melanoma network have shown that the phenotypically constrained inference obtains improved performance when we have small numbers of state transition observations.
具有规定稳态的表型约束布尔网络推理
在本文中,我们研究了一种表型约束推理算法来重建布尔网络(BNs)模型的遗传调控网络。在已有的通用最小描述长度(uMDL)网络推理算法的基础上,研究了加入基于规定吸引子或稳态的先验信息是否有助于更好地重建潜在的基因调控关系。基于随机生成网络和转移性黑色素瘤网络的实验比较了有和没有规定稳态的网络推理性能,结果表明,当我们有少量的状态转移观察时,表型约束推理获得了更好的性能。
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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