大量研究表明 microRNA (miRNA) 对于疾病的预测、诊断和表征至关重要。然而，通过传统的生物学实验来识别 miRNA 与疾病的关联既昂贵又耗时。为了进一步探索这些关联，我们提出了一种基于混合高阶矩与元素级注意机制（HHOMR）相结合的模型。该模型创新地将混合高阶统计信息与结构和群落信息融合在一起。具体来说，我们首先根据 miRNA 与疾病之间的现有关联构建了一个异质图。 HHOMR 采用结构融合层来捕获结构级嵌入，并利用混合高阶矩编码器层来增强特征。然后使用元素级注意机制来自适应地集成这些混合时刻的特征。最后，利用多层感知器来计算 miRNA 与疾病之间的关联分数。通过 HMDD v2.0 的五倍交叉验证，我们实现了 93.28% 的平均 AUC。与四种最先进的模型相比，HHOMR 表现出了优越的性能。此外，还进行了食管肿瘤、淋巴瘤和前列腺肿瘤这三种疾病的病例研究。在具有高疾病关联评分的前 50 个 miRNA 中，dbDEMC 和 miR2Disease 数据库分别确认了 46、47 和 45 个与这些疾病相关的 miRNA。我们的结果表明，HHOMR 不仅优于现有模型，而且在预测 miRNA-疾病关联方面显示出巨大潜力。© 作者 2024。由牛津大学出版社出版。

Numerous studies have demonstrated that microRNAs (miRNAs) are critically important for the prediction, diagnosis, and characterization of diseases. However, identifying miRNA-disease associations through traditional biological experiments is both costly and time-consuming. To further explore these associations, we proposed a model based on hybrid high-order moments combined with element-level attention mechanisms (HHOMR). This model innovatively fused hybrid higher-order statistical information along with structural and community information. Specifically, we first constructed a heterogeneous graph based on existing associations between miRNAs and diseases. HHOMR employs a structural fusion layer to capture structure-level embeddings and leverages a hybrid high-order moments encoder layer to enhance features. Element-level attention mechanisms are then used to adaptively integrate the features of these hybrid moments. Finally, a multi-layer perceptron is utilized to calculate the association scores between miRNAs and diseases. Through five-fold cross-validation on HMDD v2.0, we achieved a mean AUC of 93.28%. Compared with four state-of-the-art models, HHOMR exhibited superior performance. Additionally, case studies on three diseases-esophageal neoplasms, lymphoma, and prostate neoplasms-were conducted. Among the top 50 miRNAs with high disease association scores, 46, 47, and 45 associated with these diseases were confirmed by the dbDEMC and miR2Disease databases, respectively. Our results demonstrate that HHOMR not only outperforms existing models but also shows significant potential in predicting miRNA-disease associations.© The Author(s) 2024. Published by Oxford University Press.