Autism Spectrum Disorder (ASD) is a multifactorial-driven neurodevelopmental disorder, the pathophysiological mechanisms of which remain largely elusive, significantly hampering the development of effective therapeutic strategies. MicroRNAs (miRNAs) are emerging as critical regulators in the molecular etiology of autism, influencing gene expression by interacting with target mRNAs involved in neural development and synaptic function. To introduce miRNA-gene regulatory information to identify potential autism-related genes and predict drug targets, we introduce a multitask learning approach named ARGENT based on the heterogeneous graph convolutional network (HGCN). The heterogeneous graph in ARGENT includes nodes representing genes, miRNAs, drugs and integrates the regulatory relationships between miRNA-gene, gene-gene, and the associations between genes and drugs. Using the HGCN, the proposed model is able to learn embeddings for these nodes by aggregating information from different adjacent node types, enabling the prediction of autism-related genes and their potential drug candidates. The efficacy of ARGENT is substantiated through multiple validation experiments, demonstrating that it not only identifies known autism-related genes but also reveals novel candidate genes and potential drug targets. Additionally, KEGG and GO analysis results of known genes related to ASD and genes predicted by ARGENT as potentially related to ASD reveal that these genes are significantly enriched in pathways associated with neural signal transmission. This suggests that these genes may play crucial roles in the core functions and structure of neural circuits, potentially influencing the neurodevelopmental aspects of ASD, providing new insights for prevention and drug repositioning.

中文翻译：

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ARGENT：使用异构图卷积网络预测自闭症相关基因和药物靶标的多任务学习模型


自闭症谱系障碍（ASD）是一种多因素驱动的神经发育障碍，其病理生理机制在很大程度上仍然难以捉摸，极大地阻碍了有效治疗策略的开发。 MicroRNA (miRNA) 正在成为自闭症分子病因学中的关键调节因子，通过与参与神经发育和突触功能的靶 mRNA 相互作用来影响基因表达。为了引入 miRNA 基因调控信息来识别潜在的自闭症相关基因并预测药物靶点，我们引入了一种基于异构图卷积网络（HGCN）的多任务学习方法，名为 ARGENT。 ARGENT中的异构图包括代表基因、miRNA、药物的节点，并整合了miRNA-基因、基因-基因之间的调控关系以及基因与药物之间的关联。使用 HGCN，所提出的模型能够通过聚合来自不同相邻节点类型的信息来学习这些节点的嵌入，从而能够预测自闭症相关基因及其潜在候选药物。 ARGENT 的功效通过多项验证实验得到证实，证明它不仅可以识别已知的自闭症相关基因，还可以揭示新的候选基因和潜在的药物靶点。此外，已知与 ASD 相关的基因以及 ARGENT 预测的可能与 ASD 相关的基因的 KEGG 和 GO 分析结果表明，这些基因在与神经信号传递相关的通路中显着富集。这表明这些基因可能在神经回路的核心功能和结构中发挥关键作用，可能影响自闭症谱系障碍的神经发育方面，为预防和药物重新定位提供新的见解。