Human aging is linked to many prevalent diseases. The aging process is highly influenced by genetic factors. Hence, it is important to identify human aging-related genes. We focus on supervised prediction of such genes. Gene expression-based methods for this purpose study genes in isolation from each other. While protein-protein interaction (PPI) network-based methods for this purpose account for interactions between genes’ protein products, current PPI network data are context-unspecific, spanning different biological conditions. Instead, here, we focus on an aging-specific subnetwork of the entire PPI network, obtained by integrating aging-specific gene expression data and PPI network data. The potential of such data integration has been recognized but mostly in the context of cancer. So, we are the first to propose a supervised learning framework for predicting aging-related genes from an aging-specific PPI subnetwork. In a systematic and comprehensive evaluation, we find that in many of the evaluation tests: (i) using an aging-specific subnetwork indeed yields more accurate aging-related gene predictions than using the entire network, and (ii) predictive methods from our framework that have not previously been used for supervised prediction of aging-related genes outperform existing prominent methods for the same purpose. These results justify the need for our framework.

中文翻译：

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从上下文特定的蛋白质相互作用子网络对衰老相关基因进行监督预测


人类衰老与许多流行疾病有关。衰老过程很大程度上受遗传因素影响。因此，识别人类衰老相关基因非常重要。我们专注于此类基因的监督预测。为此目的，基于基因表达的方法研究彼此分离的基因。虽然用于此目的的基于蛋白质-蛋白质相互作用 (PPI) 网络的方法解释了基因蛋白质产物之间的相互作用，但当前的 PPI 网络数据与上下文无关，涵盖不同的生物条件。相反，在这里，我们关注整个 PPI 网络的衰老特定子网，该子网是通过整合衰老特定基因表达数据和 PPI 网络数据而获得的。这种数据整合的潜力已经得到认可，但主要是在癌症的背景下。因此，我们是第一个提出监督学习框架，用于从特定于衰老的 PPI 子网络预测衰老相关基因。在系统和全面的评估中，我们发现在许多评估测试中：（i）使用特定于衰老的子网络确实比使用整个网络产生更准确的衰老相关基因预测，以及（ii）我们框架中的预测方法以前从未用于衰老相关基因的监督预测的方法优于用于相同目的的现有著名方法。这些结果证明了我们框架的必要性。