Network Enhancement: a general method to denoise weighted biological networks
This method addresses noise issues in biological networks for researchers, but it is incremental as it builds on existing denoising techniques with a specific mathematical approach.
The paper tackles the problem of noise in weighted biological networks, which hampers pattern discovery, by proposing Network Enhancement (NE), a method that denoises networks and improves downstream tasks like gene function prediction and species identification.
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.