The statistics for benchmark datasets.

MicroRNAs (miRNAs) play crucial roles in cancer progression, invasion, and response to treatment, particularly in regulating anticancer drug resistance and sensitivity. Identifying potential human miRNA-drug associations (MDAs) that manifest as resistance or sensitivity relationships offers valuable insights for cancer treatment and drug development. With the growing availability of biological data, computational methods have emerged as powerful tools to complement experimental approaches. However, limited attention has been paid to computational prediction of MDAs. Furthermore, existing approaches typically rely on known MDA information, overlooking the valuable insights available from multi-source data related to miRNAs and drugs. In this study, we present a multi-view fusion-based graph convolutional network with attention mechanism (MGCNA) to predict miRNA-associated drug resistance/sensitivity. Specifically, MGCNA integrates macro- and micro- level information of miRNAs and drugs to construct multi-view node features from different perspectives. The proposed multi-view graph convolutional network (GCN) encoder obtains miRNA and disease features from different views and learns adaptive importance weights of the embedding using an attention mechanism. Extensive experiments on manually curated benchmark datasets demonstrate that MGCNA outperforms existing baseline methods. Case studies of two common drugs further establish MGCNA’s effectiveness in discovering novel MDAs.微小核糖核酸（microRNAs，miRNAs）在癌症进展、侵袭及治疗应答过程中发挥关键作用，尤其在调控抗肿瘤药物的耐药性与敏感性方面意义重大。识别可体现耐药或敏感关联的潜在人类miRNA-药物关联（miRNA-drug associations，MDAs），可为癌症治疗与药物研发提供宝贵的研究思路。随着生物数据的可获取性持续提升，计算方法已成为补充实验手段的有力工具。然而，当前针对miRNA-药物关联的计算预测研究尚未得到足够重视。此外，现有方法通常仅依赖已知的miRNA-药物关联信息，却忽视了miRNA与药物相关多源数据中蕴含的宝贵价值。本研究提出一种基于多视图融合且带有注意力机制的图卷积网络（multi-view fusion-based graph convolutional network with attention mechanism，MGCNA），用于预测与miRNA相关的药物耐药性/敏感性。具体而言，MGCNA整合miRNA与药物的宏观及微观层面信息，从不同视角构建多视图节点特征。所提出的多视图图卷积网络（graph convolutional network，GCN）编码器可从不同视图提取miRNA与疾病特征，并通过注意力机制学习嵌入向量的自适应重要性权重。在经人工整理的基准数据集上开展的大量实验表明，MGCNA的性能优于现有基线方法。针对两种常见药物的案例研究进一步验证了MGCNA在发现新型miRNA-药物关联方面的有效性。