Liu Yinbo, Wu Qi, Zhou Le, Liu Yuchen, Li Chao, Wei Zhuoyu, Peng Wei, Yue Yi, Zhu Xiaolei
School of Information and Artificial Intelligence, Anhui Agricultural University, Hefei, Anhui, 230036, China.
Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China.
BMC Genomics. 2024 Dec 2;25(1):1161. doi: 10.1186/s12864-024-11078-4.
The association between MicroRNAs (miRNAs) and diseases is crucial in treating and exploring many diseases or cancers. Although wet-lab methods for predicting miRNA-disease associations (MDAs) are effective, they are often expensive and time-consuming. Significant advancements have been made using Graph Neural Network-based methods (GNN-MDAs) to address these challenges. However, these methods still face limitations, such as not considering nodes' deep-level similarity associations and hierarchical learning patterns. Additionally, current models do not retain the memory of previously learned heterogeneous historical information about miRNAs or diseases, only focusing on parameter learning without the capability to remember heterogeneous associations.
This study introduces the K-means disentangled high-level biological similarity to utilize potential hierarchical relationships fully and proposes a Graph Attention Heterogeneous Biological Memory Network architecture (DiGAMN) with memory capabilities. Extensive experiments were conducted across four datasets, comparing the DiGAMN model and its disentangling method against ten state-of-the-art non-disentangled methods and six traditional GNNs. DiGAMN excelled, achieving AUC scores of 96.35%, 96.10%, 96.01%, and 95.89% on the Data1 to Data4 datasets, respectively, surpassing all other models. These results confirm the superior performance of DiGAMN and its disentangling method. Additionally, various ablation studies were conducted to validate the contributions of different modules within the framework, and's encoding statuses and memory units of DiGAMN were visualized to explore the utility and functionality of its modules. Case studies confirmed the effectiveness of DiGAMN's predictions, identifying several new disease-associated miRNAs.
DiGAMN introduces the use of a disentangled biological similarity approach for the first time and successfully constructs a Disentangled Graph Attention Heterogeneous Biological Memory Network model. This network can learn disentangled representations of similarity information and effectively store the potential biological entanglement information of miRNAs and diseases. By integrating disentangled similarity information with a heterogeneous attention memory network, DiGAMN enhances the model's ability to capture and utilize complex underlying biological data, significantly outperforming many existing models. The concepts used in this method also provide new perspectives for predicting miRNAs associated with diseases.
微小RNA（miRNA）与疾病之间的关联对于多种疾病或癌症的治疗和探索至关重要。尽管用于预测miRNA-疾病关联（MDA）的湿实验室方法很有效，但它们通常成本高昂且耗时。基于图神经网络的方法（GNN-MDA）在应对这些挑战方面取得了显著进展。然而，这些方法仍然存在局限性，例如没有考虑节点的深层次相似性关联和分层学习模式。此外，当前模型没有保留关于miRNA或疾病的先前学习的异构历史信息的记忆，只专注于参数学习而没有记住异构关联的能力。
本研究引入K均值解缠的高级生物相似性以充分利用潜在的层次关系，并提出了一种具有记忆能力的图注意力异构生物记忆网络架构（DiGAMN）。在四个数据集上进行了广泛的实验，将DiGAMN模型及其解缠方法与十种先进的非解缠方法和六种传统GNN进行了比较。DiGAMN表现出色，在Data1至Data4数据集上分别取得了96.35%、96.10%、96.01%和95.89%的AUC分数，超过了所有其他模型。这些结果证实了DiGAMN及其解缠方法的卓越性能。此外，进行了各种消融研究以验证框架内不同模块的贡献，并对DiGAMN的编码状态和记忆单元进行了可视化，以探索其模块的效用和功能。案例研究证实了DiGAMN预测的有效性，识别出了几种新的疾病相关miRNA。
DiGAMN首次引入了解缠的生物相似性方法，并成功构建了解缠的图注意力异构生物记忆网络模型。该网络可以学习相似性信息的解缠表示，并有效地存储miRNA和疾病的潜在生物缠结信息。通过将解缠的相似性信息与异构注意力记忆网络相结合，DiGAMN增强了模型捕获和利用复杂潜在生物数据的能力，显著优于许多现有模型。该方法中使用的概念也为预测与疾病相关的miRNA提供了新的视角。
