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Supervised prediction of aging-related genes from a context-specific protein interaction subnetwork

机译：从上下文特定的蛋白质相互作用子网络监督衰老相关基因的预测

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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. We find that using an aging-specific subnetwork indeed yields more accurate aging-related gene predictions than using the entire network. Also, 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.
机译：人类衰老与许多流行疾病有关。衰老过程受遗传因素的影响很大。因此，鉴定与人类衰老相关的基因很重要。我们专注于此类基因的监督预测。基于基因表达的方法为此目的研究彼此隔离的基因。虽然基于蛋白质-蛋白质相互作用（PPI）网络的方法可解决基因蛋白质产物之间的相互作用，但当前的PPI网络数据是上下文不确定的，涵盖了不同的生物学条件。取而代之的是，在这里，我们专注于整个PPI网络的特定于衰老的子网，该子网络是通过整合特定于衰老的基因表达数据和PPI网络数据而获得的。人们已经认识到这种数据集成的潜力，但主要是在癌症的背景下。因此，我们是第一个提出有监督的学习框架，用于从特定于衰老的PPI子网中预测与衰老相关的基因。我们发现，使用特定于衰老的子网络确实比使用整个网络能产生更准确的与衰老相关的基因预测。同样，从我们的框架中获得的，以前未用于监督与衰老相关的基因的预测方法，也比出于相同目的的现有突出方法要好。这些结果证明需要我们的框架。

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