局部偏相关网络重构的比较研究
Network reconstruction with local partial correlation: a comparative evaluation
摘要：
在过去的十年中，已经提出了各种方法来重建网络建模为高斯图形模型。在这项工作中，我们分析了三种不同的方法：图形套索（GLasso），图形岭（GGMridge）和局部部分相关（LPC）。为了评估方法，我们使用了从模拟随机图（Erdös-Rényi，Barabási-Albert，Watts-Strogatz）生成的高维数据。通过受试者工作特征（ROC）曲线评估性能。此外，该方法被用来重建共表达网络的差异表达基因在人类宫颈癌的数据。在大多数模拟情况下，LPC方法优于GLasso。GGMridge比其他两种方法产生更好的ROC曲线。最后，LPC和GGMridge在真实数据研究中获得了类似的结果。
英文摘要：
Over the past decade, various methods have been proposed for the reconstruction of networks modeled as Gaussian Graphical Models. In this work, we analyzed three different approaches: the Graphical Lasso (GLasso), the Graphical Ridge (GGMridge), and the Local Partial Correlation (LPC). For the evaluation of the methods, we used high dimensional data generated from simulated random graphs (Erdös-Rényi, Barabási-Albert, Watts-Strogatz). The performance was assessed through the Receiver Operating Characteristic (ROC) curve. In addition, the methods were used to reconstruct the co-expression network for differentially expressed genes in human cervical cancer data. The LPC method outperformed the GLasso in most simulated cases. The GGMridge produced better ROC curves then both the other methods. Finally, LPC and GGMridge obtained similar outcomes in real data studies.
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。
