发表机构
L3S Research Center, Leibniz University Hannover; Lower Saxony Center for Artificial Intelligence and Causal Methods in Medicine (CAIMed); Institute for Causal and Explainable Artificial Intelligence in Life Sciences; Center for Computational Life Sciences; RWTH Aachen University（莱布尼茨汉诺威大学L3S研究中心; 下萨克森州人工智能与因果医学方法中心（CAIMed）; 生命科学因果与可解释人工智能研究所; 计算生命科学中心; 亚琛工业大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出GO4CD算法，利用基因本体论构建生物学意义的基因分区，与CLOC集成，在模拟数据上显著降低不可容许率，更适用于学习聚类级因果基因调控网络。
AI 中文摘要
在高维基因调控网络（GRN）中发现因果关系在计算上具有挑战性，且由于密集的连接往往难以解释。因此，将基因分组为功能模块可以提高可处理性和生物学可解释性。然而，现有的聚类级因果发现方法假设可以访问预定义的可容许分区，要求聚类上的图是无环的。因此，构建这样的分区具有挑战性。在本工作中，我们引入了基于GO的因果发现聚类（GO4CD），一种使用基因本体论（GO）在多个粒度级别构建生物学上有意义的基因分区的算法，同时偏向于更可能对因果发现可容许的分区。GO4CD将参与共享生物学过程的基因分组在一起，并通过本体层级传播基因注释以实现不同粒度的分区。此外，我们将GO4CD与聚类上的因果学习（CLOC）算法集成，并使用条件独立性预言机和模拟基因表达数据上的多变量条件独立性检验来评估真实马尔可夫等价类的恢复。我们在多个http URL调控子网络上评估GO4CD，发现它在18.1%的情况下不可容许，而语义相似性基线为65.3-82.3%。我们的结果表明，GO4CD在基于生物学上有意义的基因聚类学习因果GRN方面明显更适用。
英文摘要
Discovery of causal relationships in high-dimensional Gene Regulatory Networks (GRN) is computationally challenging and often difficult to interpret due to dense connections. Therefore, grouping genes together into functional modules can improve tractability and biological interpretability. However, existing cluster level causal discovery methods assume access to a predefined admissible partitions, requiring the graph over clusters to be acyclic. Constructing such partitions is therefore challenging. In this work, we introduce GO-based Clustering for Causal Discovery (GO4CD), an algorithm that uses Gene Ontology (GO) to construct biologically meaningful gene partitions at multiple levels of granularity, while favoring those more likely to be admissible for causal discovery. GO4CD groups together genes participating in a shared biological process, and propagates gene annotations through the ontology hierarchy to achieve different granularity of partitions. Furthermore, we integrate GO4CD with Causal Learning over Clusters (CLOC) algorithm and evaluate recovery of true Markov equivalence class both with an oracle of conditional independencies and on simulated gene expression data using multivariate conditional independence tests. We evaluate GO4CD on multiple E.coli regulatory subnetworks and find that it is inadmissible in 18.1% of the cases, compared with 65.3-82.3% for the semantic-similarity baselines. Our results indicate that GO4CD is substantially better suited to learning causal GRNs defined over biologically meaningful gene clusters.
