Inference of regulatory networks through temporally sparse data
Inference of regulatory networks through temporally sparse data
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DOI：
10.3389/fcteg.2022.1017256
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发表时间：
2022-07
期刊：
影响因子：
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通讯作者：
Mohammad Alali;Mahdi Imani
中科院分区：
文献类型：
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作者：
Mohammad Alali;Mahdi Imani
基因组学的一个主要目标是正确地捕捉基因调控网络（grn）的复杂动态行为。这包括推断基因之间复杂的相互作用，可用于广泛的基因组学分析，包括疾病的诊断或预后，以及寻找癌症等慢性疾病的有效治疗方法。布尔网络已经成为捕获grn行为的一类成功模型。在大多数实际设置中，grn的推断应该通过有限的和暂时稀疏的基因组数据来实现。grn中大量的基因导致可能的拓扑候选空间很大，由于计算资源的限制，往往无法对其进行穷尽搜索。本文利用贝叶斯优化和基于核的方法开发了一种可扩展的、高效的grn拓扑推理方法。该方法不是对可能的拓扑进行穷举搜索，而是构建一个具有拓扑启发核函数的高斯过程（GP）来解释似然函数中的相关性。然后，利用GP模型的后验分布，通过优化勘探和开采之间的平衡，有效地搜索似然值最高的拓扑结构。通过一个众所周知的哺乳动物细胞周期网络的综合数值实验证明了该方法的性能。
A major goal in genomics is to properly capture the complex dynamical behaviors of gene regulatory networks (GRNs). This includes inferring the complex interactions between genes, which can be used for a wide range of genomics analyses, including diagnosis or prognosis of diseases and finding effective treatments for chronic diseases such as cancer. Boolean networks have emerged as a successful class of models for capturing the behavior of GRNs. In most practical settings, inference of GRNs should be achieved through limited and temporally sparse genomics data. A large number of genes in GRNs leads to a large possible topology candidate space, which often cannot be exhaustively searched due to the limitation in computational resources. This paper develops a scalable and efficient topology inference for GRNs using Bayesian optimization and kernel-based methods. Rather than an exhaustive search over possible topologies, the proposed method constructs a Gaussian Process (GP) with a topology-inspired kernel function to account for correlation in the likelihood function. Then, using the posterior distribution of the GP model, the Bayesian optimization efficiently searches for the topology with the highest likelihood value by optimally balancing between exploration and exploitation. The performance of the proposed method is demonstrated through comprehensive numerical experiments using a well-known mammalian cell-cycle network.