IEEE/ACM Trans Comput Biol Bioinform. 2023 Nov-Dec;20(6):3363-3375. doi: 10.1109/TCBB.2022.3187739. Epub 2023 Dec 25.
MiRNAs are reported to be linked to the pathogenesis of human complex diseases. Disease-related miRNAs may serve as novel bio-marks and drug targets. This work focuses on designing a multi-relational Graph Convolutional Network model to predict miRNA-disease associations (HGCNMDA) from a Heterogeneous network. HGCNMDA introduces a gene layer to construct a miRNA-gene-disease heterogeneous network. We refine the features of nodes into initial and inductive features so that the direct and indirect associations between diseases and miRNA can be considered simultaneously. Then HGCNMDA learns feature embeddings for miRNAs and disease through a multi-relational graph convolutional network model that can assign appropriate weights to different types of edges in the heterogeneous network. Finally, the miRNA-disease associations were decoded by the inner product between miRNA and disease feature embeddings. We apply our model to predict human miRNA-disease associations. The HGCNMDA is superior to the other state-of-the-art models in identifying missing miRNA-disease associations and also performs well on recommending related miRNAs/diseases to new diseases/ miRNAs. The codes are available at https://github.com/weiba/HGCNMDA.
miRNAs 被报道与人类复杂疾病的发病机制有关。与疾病相关的 miRNAs 可以作为新的生物标志物和药物靶点。本工作重点设计了一个多关系图卷积网络模型，从异构网络中预测 miRNA-疾病关联（HGCNMDA）。HGCNMDA 引入基因层来构建 miRNA-基因-疾病异质网络。我们将节点的特征细化为初始特征和诱导特征，以便同时考虑疾病和 miRNA 之间的直接和间接关联。然后，HGCNMDA 通过一个多关系图卷积网络模型学习 miRNA 和疾病的特征嵌入，该模型可以为异质网络中的不同类型边分配适当的权重。最后，通过 miRNA 和疾病特征嵌入之间的内积解码 miRNA-疾病关联。我们将我们的模型应用于预测人类 miRNA-疾病关联。HGCNMDA 在识别缺失的 miRNA-疾病关联方面优于其他最先进的模型，并且在向新疾病/miRNA 推荐相关 miRNA/疾病方面也表现良好。代码可在 https://github.com/weiba/HGCNMDA 获得。
