Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing, and the College of Information Science and Engineering in Hunan Normal University, P.R. China.
College of Information Science and Engineering, Hunan Normal University, Changsha, China.
Brief Bioinform. 2022 May 13;23(3). doi: 10.1093/bib/bbac137.
The roles of brain regions activities and gene expressions in the development of Alzheimer's disease (AD) remain unclear. Existing imaging genetic studies usually has the problem of inefficiency and inadequate fusion of data. This study proposes a novel deep learning method to efficiently capture the development pattern of AD. First, we model the interaction between brain regions and genes as node-to-node feature aggregation in a brain region-gene network. Second, we propose a feature aggregation graph convolutional network (FAGCN) to transmit and update the node feature. Compared with the trivial graph convolutional procedure, we replace the input from the adjacency matrix with a weight matrix based on correlation analysis and consider common neighbor similarity to discover broader associations of nodes. Finally, we use a full-gradient saliency graph mechanism to score and extract the pathogenetic brain regions and risk genes. According to the results, FAGCN achieved the best performance among both traditional and cutting-edge methods and extracted AD-related brain regions and genes, providing theoretical and methodological support for the research of related diseases.
大脑区域活动和基因表达在阿尔茨海默病（AD）发展中的作用尚不清楚。现有的影像遗传学研究通常存在效率低下和数据融合不足的问题。本研究提出了一种新的深度学习方法，以有效地捕捉 AD 的发展模式。首先，我们将大脑区域和基因之间的相互作用建模为大脑区域-基因网络中的节点到节点特征聚合。其次，我们提出了一种特征聚合图卷积网络（FAGCN）来传输和更新节点特征。与简单的图卷积过程相比，我们用基于相关分析的权重矩阵代替邻接矩阵作为输入，并考虑共同邻居相似性，以发现节点更广泛的关联。最后，我们使用全梯度显著性图机制对致病的大脑区域和风险基因进行评分和提取。根据结果，FAGCN 在传统和前沿方法中表现最佳，并提取了与 AD 相关的大脑区域和基因，为相关疾病的研究提供了理论和方法学支持。
