Network Enhancement：一种加权生物网络去噪的通用方法
Network Enhancement: a general method to denoise weighted biological networks
摘要：
网络在生物学中无处不在，它们在从分子到生物群系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化，生物网络是嘈杂的，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），一种用于提高无向加权网络的信噪比的方法。NE使用一个双随机矩阵算子，诱导稀疏性，并提供了一个封闭形式的解决方案，增加输入网络的频谱特征间隙。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，简化来自人类基因组的嘈杂Hi-C接触图的解释，并提高物种的细粒度识别准确性。我们的研究结果表明，NE是广泛适用于去噪生物网络。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
