College of Computer Science and Technology, Qingdao University, Qingdao, 266071, Shandong, China.
BMC Bioinformatics. 2024 Jun 14;25(1):214. doi: 10.1186/s12859-024-05841-3.
The exploration of gene-disease associations is crucial for understanding the mechanisms underlying disease onset and progression, with significant implications for prevention and treatment strategies. Advances in high-throughput biotechnology have generated a wealth of data linking diseases to specific genes. While graph representation learning has recently introduced groundbreaking approaches for predicting novel associations, existing studies always overlooked the cumulative impact of functional modules such as protein complexes and the incompletion of some important data such as protein interactions, which limits the detection performance.
Addressing these limitations, here we introduce a deep learning framework called ModulePred for predicting disease-gene associations. ModulePred performs graph augmentation on the protein interaction network using L3 link prediction algorithms. It builds a heterogeneous module network by integrating disease-gene associations, protein complexes and augmented protein interactions, and develops a novel graph embedding for the heterogeneous module network. Subsequently, a graph neural network is constructed to learn node representations by collectively aggregating information from topological structure, and gene prioritization is carried out by the disease and gene embeddings obtained from the graph neural network. Experimental results underscore the superiority of ModulePred, showcasing the effectiveness of incorporating functional modules and graph augmentation in predicting disease-gene associations. This research introduces innovative ideas and directions, enhancing the understanding and prediction of gene-disease relationships.
探索基因-疾病关联对于理解疾病发病和进展的机制至关重要，对预防和治疗策略具有重要意义。高通量生物技术的进步已经产生了大量将疾病与特定基因联系起来的数据。虽然图表示学习最近为预测新的关联引入了开创性的方法，但现有研究总是忽略了功能模块（如蛋白质复合物）的累积影响以及一些重要数据（如蛋白质相互作用）的不完整性，这限制了检测性能。
为了解决这些限制，我们在这里引入了一个称为 ModulePred 的深度学习框架，用于预测疾病-基因关联。ModulePred 使用 L3 链接预测算法对蛋白质相互作用网络进行图增强。它通过整合疾病-基因关联、蛋白质复合物和增强的蛋白质相互作用来构建异构模块网络，并为异构模块网络开发新的图嵌入。然后，构建一个图神经网络，通过从拓扑结构中集体聚合信息来学习节点表示，并通过从图神经网络获得的疾病和基因嵌入进行基因优先级排序。实验结果强调了 ModulePred 的优越性，展示了在预测疾病-基因关联中纳入功能模块和图增强的有效性。这项研究引入了创新的思路和方向，增强了对基因-疾病关系的理解和预测。
