GRASMOS: Graph Signage Model Selection for Gene Regulatory Networks
GRASMOS: Graph Signage Model Selection for Gene Regulatory Networks
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DOI：
10.1609/aaai.v37i10.26457
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发表时间：
2022-11
期刊：
影响因子：
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通讯作者：
A. Brilliantova;Hannah Miller;Ivona Bez'akov'a
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文献类型：
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作者：
A. Brilliantova;Hannah Miller;Ivona Bez'akov'a
签名网络（具有正边和负边的网络）通常出现在从分子生物学到社交媒体的各个领域。边缘标志--即，图形标志--表示顶点之间的交互模式，并且可以提供对底层系统形成过程的洞察。考虑标志形成的生成模型对于测试关于交互出现的假设和创建用于算法基准测试的合成数据集（特别是在难以获得真实世界数据集的领域）至关重要。在这项工作中，我们提出了一种新的基于极大似然的优化问题，用于在给定拓扑结构的情况下对信号进行建模，并在基因调控的背景下展示它。基因间的相互调节作用在生物体发育过程中起着关键作用，一旦被破坏，就会导致严重的生物体异常和疾病。我们的贡献有三个方面：首先，我们设计了一类新的标志模型，为一个给定的拓扑结构，并根据参数设置，我们讨论了它的基因调控网络（GRNs）的生物学解释。其次，我们设计算法计算最大似然-根据参数设置，我们的算法范围从封闭形式的表达式MCMC采样。第三，我们评估了我们的算法在合成数据集和真实世界的大型GRN上的结果。我们的工作可以预测未知的基因调控，新的生物学假设，以及基因调控领域的现实基准数据集。
Signed networks (networks with positive and negative edges) commonly arise in various domains from molecular biology to social media. The edge signs -- i.e., the graph signage -- represent the interaction pattern between the vertices and can provide insights into the underlying system formation process. Generative models considering signage formation are essential for testing hypotheses about the emergence of interactions and for creating synthetic datasets for algorithm benchmarking (especially in areas where obtaining real-world datasets is difficult). In this work, we pose a novel Maximum-Likelihood-based optimization problem for modeling signages given their topology and showcase it in the context of gene regulation. Regulatory interactions of genes play a key role in the process of organism development, and when broken can lead to serious organism abnormalities and diseases. Our contributions are threefold: First, we design a new class of signage models for a given topology, and, based on the parameter setting, we discuss its biological interpretations for gene regulatory networks (GRNs). Second, we design algorithms computing the Maximum Likelihood -- depending on the parameter setting, our algorithms range from closed-form expressions to MCMC sampling. Third, we evaluated the results of our algorithms on synthetic datasets and real-world large GRNs. Our work can lead to the prediction of unknown gene regulations, novel biological hypotheses, and realistic benchmark datasets in the realm of gene regulation.