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

TL;DR

This paper introduces a supervised learning framework that leverages an aging-specific protein interaction subnetwork, derived from gene expression and PPI data, to improve the prediction of aging-related genes over traditional methods.

Contribution

It is the first to utilize an aging-specific PPI subnetwork in a supervised learning framework for predicting aging-related genes, demonstrating improved accuracy over existing methods.

Findings

Aging-specific subnetworks improve prediction accuracy.

New framework outperforms existing methods.

Supervised approach benefits from context-specific data.

Abstract

Background. 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. Results. 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. Conclusion. These results justify the need for our framework.