School of Computer Science and Engineering, Hunan Provincial Key Lab on Bioinformatics, Central South University, Changsha, Hunan 410083, P.R. China.
School of Mathematics and Computational Science, National Center for Applied Mathematics in Hunan, Xiangtan University, Xiangtan, Hunan 411105, P.R. China.
Brief Bioinform. 2024 Nov 22;26(1). doi: 10.1093/bib/bbae611.
Alzheimer's disease (AD) is a complex disease with its genetic etiology not fully understood. Gene network-based methods have been proven promising in predicting AD genes. However, existing approaches are limited in their ability to model the nonlinear relationship between networks and disease genes, because (i) any data can be theoretically decomposed into the sum of a linear part and a nonlinear part, (ii) the linear part can be best modeled by a linear model since a nonlinear model is biased and can be easily overfit, and (iii) existing methods do not separate the linear part from the nonlinear part when building the disease gene prediction model. To address the limitation, we propose linear model-integrated graph convolutional network (LIMO-GCN), a generic disease gene prediction method that models the data linearity and nonlinearity by integrating a linear model with GCN. The reason to use GCN is that it is by design naturally suitable to dealing with network data, and the reason to integrate a linear model is that the linearity in the data can be best modeled by a linear model. The weighted sum of the prediction of the two components is used as the final prediction of LIMO-GCN. Then, we apply LIMO-GCN to the prediction of AD genes. LIMO-GCN outperforms the state-of-the-art approaches including GCN, network-wide association studies, and random walk. Furthermore, we show that the top-ranked genes are significantly associated with AD based on molecular evidence from heterogeneous genomic data. Our results indicate that LIMO-GCN provides a novel method for prioritizing AD genes.
阿尔茨海默病（AD）是一种复杂的疾病，其遗传病因尚未完全了解。基于基因网络的方法已被证明在预测 AD 基因方面具有很大的潜力。然而，现有的方法在建模网络和疾病基因之间的非线性关系方面能力有限，因为 (i) 任何数据都可以理论上分解为线性部分和非线性部分的和，(ii) 线性部分可以通过线性模型最佳建模，因为非线性模型存在偏差，并且容易过拟合，以及 (iii) 现有方法在构建疾病基因预测模型时没有将线性部分与非线性部分分开。为了解决这个限制，我们提出了线性模型集成图卷积网络（LIMO-GCN），这是一种通用的疾病基因预测方法，通过将线性模型与 GCN 集成来对数据的线性和非线性进行建模。使用 GCN 的原因是它的设计天然适用于处理网络数据，而集成线性模型的原因是数据中的线性部分可以通过线性模型最佳建模。两个组件的预测的加权和作为 LIMO-GCN 的最终预测。然后，我们将 LIMO-GCN 应用于 AD 基因的预测。LIMO-GCN 优于包括 GCN、全网络关联研究和随机游走在内的最先进的方法。此外，我们表明，根据来自异质基因组数据的分子证据，排名靠前的基因与 AD 显著相关。我们的结果表明，LIMO-GCN 为 AD 基因的优先级排序提供了一种新方法。
