Biomedical studies have revealed the crucial role of miRNAs in the progression of many diseases, and computational prediction methods are increasingly proposed for assisting biological experiments to verify miRNA-disease associations (MDAs). The generalizability is a significant issue, the prediction ought to be available for entities with fewer or without existing MDAs, while it is previously underemphasized. In this study, we work on the stages of data, model, and result analysis. First, we integrate multi-source data into a miRNA-PCG-disease graph, embracing all authoritative recorded human miRNAs and diseases, and the verified MDAs are split by time and known degree as a benchmark. Second, we propose an end-to-end data-driven model that avoids taking the existing MDAs as an input feature. It performs node feature encoding, graph structure learning, and binary prediction centered on a heterogeneous graph transformer. Finally, computational experiments indicate that our method achieves state-of-the-art performance on basic metrics and effectively alleviates the neglect of less and zero known miRNAs and diseases. Predictions are conducted on all human miRNA-disease pairs, case studies further demonstrate that we can make reliable MDA detections on unseen diseases, and the prediction basis is instance-level explainable.

翻译：生物医学研究揭示了miRNA在多种疾病进展中的关键作用，为辅助生物实验验证miRNA-疾病关联（MDA），计算预测方法日益受到关注。可泛化性是一个重要问题——预测应对缺乏或没有已知MDA的实体同样有效，但此前这一特性未得到充分重视。本研究从数据、模型及结果分析三个阶段开展工作。首先，我们将多源数据整合为miRNA-PCG-疾病异构图，涵盖所有权威记录的人类miRNA与疾病，并依据时间与已知程度对已验证的MDA进行划分以构建基准数据集。其次，我们提出一种端到端的数据驱动模型，该模型避免将现有MDA作为输入特征，通过异构图变换器实现节点特征编码、图结构学习及二分类预测。计算实验表明，本方法在基础指标上达到最优性能，并有效缓解了对已知miRNA与疾病较少的实体以及零已知实体的忽略问题。我们对所有人类miRNA-疾病对进行了预测，案例研究进一步证明该方法能够对未知疾病进行可靠的MDA检测，且预测依据具有实例级可解释性。