Networks are ubiquitous in biology where they encode connectivity patterns at all scales URLanization, from molecular to the biome. However, biological networks are noisy due to the limitations of the technology used to generate them as well as inherent variation within samples. The presence of high levels of noise can hamper discovery of  patterns and dynamics encapsulated by these networks. Here we propose Network Enhancement (NE), a novel method for improving the signal-to-noise ratio of undirected, weighted networks , and thereby improving the performance of downstream analysis. NE applies a novel operator that induces sparsity and leverages higher-order networkstructures to remove weak edgesand enhance real connections. This iterative approach has a closed-form solution at convergence with desirable performanceproperties. We demonstrate the effectiveness of NE in denoising biological networks for several challenging yet important problems. Our experiments show that NE improves gene function prediction by denoising interaction networks from 22 human tissues. Further, we use NE to interpret noisy Hi-C contact maps from the human genome and demonstrate its utility across varying degrees of data quality. Finally, when applied to fine-grained species identification , NE outperforms alternative approaches by a significant margin. Taken together, our results indicate that NE is widely applicable for denoising weighted biological networks, especially when they contain high levels of noise .
Networks are ubiquitous in biology where they encode connectivity patterns at all scales URLanization, 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 .
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leverages higher-order networkstructures to remove weak edgesand enhance real connections. This iterative approach has a
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solution at convergence with desirable performanceproperties. We demonstrate the effectiveness of NE in denoising biological networks for several challenging yet important problems. Our experiments
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
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interaction networks from 22 human tissues. Further, we use NE to interpret
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and demonstrate its utility across varying degrees of data quality. Finally, when applied to
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species identification , NE outperforms alternative approaches by a significant margin. Taken together, our
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weighted biological networks, especially when they contain high levels of noise
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