Department of Computer Science, Stanford University, 353 Serra Mall, Stanford, 94305, CA, USA.
Department of Physics, Stanford University, 382 Via Pueblo Mall, Stanford, 94305, CA, USA.
Nat Commun. 2018 Aug 6;9(1):3108. doi: 10.1038/s41467-018-05469-x.
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.
网络在生物学中无处不在，它们在从分子到生物群落的所有组织尺度上编码连接模式。然而，由于测量技术的限制和固有的自然变异，生物网络存在噪声，这可能会阻碍网络模式和动态的发现。我们提出了网络增强（NE）方法，用于提高无向加权网络的信噪比。NE 使用双随机矩阵运算符，该运算符诱导稀疏性并提供一个闭式解，从而增加输入网络的特征值差距。因此，NE 去除了弱边，增强了真实连接，并导致更好的下游性能。实验表明，NE 通过对组织特异性相互作用网络进行去噪来改善基因功能预测，减轻了人类基因组中噪声 Hi-C 接触图的解释难度，并提高了物种的细粒度识别准确性。我们的结果表明，NE 广泛适用于生物网络的去噪。
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