English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Networks are ubiquitous in biology where they encode connectivity patterns atall scales of organization, from molecular to the biome. However, biologicalnetworks are noisy due to the limitations of measurement technology andinherent natural variation, which can hamper discovery of network patterns anddynamics. We propose Network Enhancement (NE), a method for improving thesignal-to-noise ratio of undirected, weighted networks. NE uses a doublystochastic matrix operator that induces sparsity and provides a closed-formsolution that increases spectral eigengap of the input network. As a result, NEremoves weak edges, enhances real connections, and leads to better downstreamperformance. Experiments show that NE improves gene function prediction bydenoising tissue-specific interaction networks, alleviates interpretation ofnoisy Hi-C contact maps from the human genome, and boosts fine-grainedidentification accuracy of species. Our results indicate that NE is widelyapplicable for denoising biological networks.

Network Enhancement: a general method to denoise weighted biological networks