Network Enhancement: a general method to denoise weighted biological networks

TL;DR

Network Enhancement (NE) is a novel method that denoises biological networks by increasing spectral eigengap, removing weak edges, and enhancing true connections, thereby improving downstream biological analysis tasks.

Contribution

NE introduces a general, closed-form spectral method for denoising weighted biological networks, improving signal-to-noise ratio and downstream analysis performance.

Findings

Improves gene function prediction in tissue-specific networks

Enhances interpretation of noisy Hi-C contact maps

Boosts species identification accuracy

Abstract

Networks are ubiquitous in biology where they encode connectivity patterns at all scales of organization, from molecular to the biome. However, biological networks are noisy due to the limitations of measurement technology and inherent natural variation, which can hamper discovery of network patterns and dynamics. We propose Network Enhancement (NE), a method for improving the signal-to-noise ratio of undirected, weighted networks. NE uses a doubly stochastic matrix operator that induces sparsity and provides a closed-form solution that increases spectral eigengap of the input network. As a result, NE removes weak edges, enhances real connections, and leads to better downstream performance. Experiments show that NE improves gene function prediction by denoising tissue-specific interaction networks, alleviates interpretation of noisy Hi-C contact maps from the human genome, and boosts fine-grained identification accuracy of species. Our results indicate that NE is widely applicable for denoising biological networks.