Chang Zhenghua, Zhu Rong, Liu Jinxing, Shang Junliang, Dai Lingyun
School of Computer Science, Qufu Normal University, Rizhao 276826, China.
Noncoding RNA. 2024 Jan 26;10(1):9. doi: 10.3390/ncrna10010009.
Biological research has demonstrated the significance of identifying miRNA-disease associations in the context of disease prevention, diagnosis, and treatment. However, the utilization of experimental approaches involving biological subjects to infer these associations is both costly and inefficient. Consequently, there is a pressing need to devise novel approaches that offer enhanced accuracy and effectiveness. Presently, the predominant methods employed for predicting disease associations rely on Graph Convolutional Network (GCN) techniques. However, the Graph Convolutional Network algorithm, which is locally aggregated, solely incorporates information from the immediate neighboring nodes of a given node at each layer. Consequently, GCN cannot simultaneously aggregate information from multiple nodes. This constraint significantly impacts the predictive efficacy of the model. To tackle this problem, we propose a novel approach, based on HyperGCN and Sørensen-Dice loss (HGSMDA), for predicting associations between miRNAs and diseases. In the initial phase, we developed multiple networks to represent the similarity between miRNAs and diseases and employed GCNs to extract information from diverse perspectives. Subsequently, we draw into HyperGCN to construct a miRNA-disease heteromorphic hypergraph using hypernodes and train GCN on the graph to aggregate information. Finally, we utilized the Sørensen-Dice loss function to evaluate the degree of similarity between the predicted outcomes and the ground truth values, thereby enabling the prediction of associations between miRNAs and diseases. In order to assess the soundness of our methodology, an extensive series of experiments was conducted employing the Human MicroRNA Disease Database (HMDD v3.2) as the dataset. The experimental outcomes unequivocally indicate that HGSMDA exhibits remarkable efficacy when compared to alternative methodologies. Furthermore, the predictive capacity of HGSMDA was corroborated through a case study focused on colon cancer. These findings strongly imply that HGSMDA represents a dependable and valid framework, thereby offering a novel avenue for investigating the intricate association between miRNAs and diseases.
生物学研究已经证明了在疾病预防、诊断和治疗背景下识别miRNA与疾病关联的重要性。然而，利用涉及生物对象的实验方法来推断这些关联既昂贵又低效。因此，迫切需要设计出具有更高准确性和有效性的新方法。目前，用于预测疾病关联的主要方法依赖于图卷积网络（GCN）技术。然而，局部聚合的图卷积网络算法在每一层仅纳入给定节点的直接相邻节点的信息。因此，GCN无法同时聚合来自多个节点的信息。这一限制显著影响了模型的预测效果。为了解决这个问题，我们提出了一种基于超图卷积网络（HyperGCN）和 Sørensen-Dice 损失（HGSMDA）的新方法，用于预测miRNA与疾病之间的关联。在初始阶段，我们开发了多个网络来表示miRNA与疾病之间的相似性，并使用GCN从不同角度提取信息。随后，我们引入HyperGCN，使用超节点构建miRNA-疾病异构图，并在该图上训练GCN以聚合信息。最后，我们利用Sørensen-Dice损失函数来评估预测结果与真实值之间的相似程度，从而实现对miRNA与疾病关联的预测。为了评估我们方法的合理性，我们以人类微小RNA疾病数据库（HMDD v3.2）作为数据集进行了一系列广泛的实验。实验结果明确表明，与其他方法相比，HGSMDA具有显著的效果。此外，通过一项针对结肠癌的案例研究，证实了HGSMDA的预测能力。这些发现强烈表明，HGSMDA代表了一个可靠且有效的框架，从而为研究miRNA与疾病之间的复杂关联提供了一条新途径。
