论文标题
通过加权动态网络分析改善了对衰老相关基因的监督预测
Improved supervised prediction of aging-related genes via weighted dynamic network analysis
论文作者
论文摘要
这项研究重点介绍了来自 - 组学数据对与衰老相关基因的预测的任务。 Unlike gene expression methods for this task that capture aging-specific information but ignore interactions between genes (i.e., their protein products), or protein-protein interaction (PPI) network methods for this task that account for PPIs but the PPIs are context-unspecific, we recently integrated the two data types into an aging-specific PPI subnetwork, which yielded more accurate aging-related gene predictions.但是，尽管衰老过程是动态的，但与静态衰老特异性子网相比，动态衰老特异性的子网并未提高预测性能。这可能是因为使用幼稚的诱导子图方法推断动态子网。取而代之的是，我们最近使用方法论上更高级的网络传播概念（NP）推断出一种动态衰老的子网，该概念在诱导的动态衰老特异性子网络中改进了不同的任务，该任务是对老化过程的无监督分析的。在这里，我们评估了我们现有的基于NP的动态子网是否会改善由诱导方法构成的动态和静态子网，这是对衰老相关基因的监督预测的经过考虑的任务。现有的基于NP的子网未加权，即，它对特定于衰老的PPI赋予了同等的重要性。由于对特定衰老的边缘权重的考虑可能很重要，因此我们还提出了一个基于加权NP的动态衰老特异性子网。我们证明，与在现有的未加权动态或静态子网络上运行的预测模型相比，在预测与衰老相关的基因时，对加权子网进行训练和测试的预测机学习模型，无论是使用NP推断现有子网络还是使用NP或诱导的方法推断出现有的子网络。
This study focuses on the task of supervised prediction of aging-related genes from -omics data. Unlike gene expression methods for this task that capture aging-specific information but ignore interactions between genes (i.e., their protein products), or protein-protein interaction (PPI) network methods for this task that account for PPIs but the PPIs are context-unspecific, we recently integrated the two data types into an aging-specific PPI subnetwork, which yielded more accurate aging-related gene predictions. However, a dynamic aging-specific subnetwork did not improve prediction performance compared to a static aging-specific subnetwork, despite the aging process being dynamic. This could be because the dynamic subnetwork was inferred using a naive Induced subgraph approach. Instead, we recently inferred a dynamic aging-specific subnetwork using a methodologically more advanced notion of network propagation (NP), which improved upon Induced dynamic aging-specific subnetwork in a different task, that of unsupervised analyses of the aging process. Here, we evaluate whether our existing NP-based dynamic subnetwork will improve upon the dynamic as well as static subnetwork constructed by the Induced approach in the considered task of supervised prediction of aging-related genes. The existing NP-based subnetwork is unweighted, i.e., it gives equal importance to each of the aging-specific PPIs. Because accounting for aging-specific edge weights might be important, we additionally propose a weighted NP-based dynamic aging-specific subnetwork. We demonstrate that a predictive machine learning model trained and tested on the weighted subnetwork yields higher accuracy when predicting aging-related genes than predictive models run on the existing unweighted dynamic or static subnetworks, regardless of whether the existing subnetworks were inferred using NP or the Induced approach.
