StabJGL：一种用于多网络重建中稀疏性和相似性选择的稳定性方法
StabJGL: a stability approach to sparsity and similarity selection in multiple network reconstruction
AI总结：
针对高维组学数据中联合图形套索参数选择不佳的问题，提出结合模型稳定性与似然相似性选择的stabJGL方法，提升多网络重建性能并发现新关联。
摘要：
近年来，网络模型因其捕捉复杂关联的能力而受到重视。在统计组学中，网络可用于建模和研究基因、蛋白质及其他类型组学数据之间的功能关系。若假设高斯图模型，则基因关联网络可由数据协方差矩阵的逆矩阵中的非零项确定。由于此类问题的高维特性，利用多个图结构之间相似性的整合方法日益流行。联合图形套索（joint graphical lasso）是实现此目的的有力工具，然而，当前用于调整网络稀疏性和相似性的基于AIC的选择准则在高维设置下表现不佳。我们提出stabJGL，该方法为联合图形套索配备了一种稳定且准确的惩罚参数选择方法，将模型稳定性概念与基于似然的相似性选择相结合。所提出的方法使强大的联合图形套索可用于组学设置，并在我们考虑的所有性能指标上优于标准联合图形套索以及最先进的联合方法。将stabJGL应用于一项泛癌研究中的蛋白质组学数据，我们展示了该方法带来的新发现的潜力。一个用户友好的stabJGL R包及教程可在Github上获取：https://github.com/Camiling/stabJGL。
英文摘要：
In recent years, network models have gained prominence for their ability to capture complex associations. In statistical omics, networks can be used to model and study the functional relationships between genes, proteins, and other types of omics data. If a Gaussian graphical model is assumed, a gene association network can be determined from the non-zero entries of the inverse covariance matrix of the data. Due to the high-dimensional nature of such problems, integrative methods that leverage similarities between multiple graphical structures have become increasingly popular. The joint graphical lasso is a powerful tool for this purpose, however, the current AIC-based selection criterion used to tune the network sparsities and similarities leads to poor performance in high-dimensional settings. We propose stabJGL, which equips the joint graphical lasso with a stable and accurate penalty parameter selection approach that combines the notion of model stability with likelihood-based similarity selection. The resulting method makes the powerful joint graphical lasso available for use in omics settings, and outperforms the standard joint graphical lasso, as well as state-of-the-art joint methods, in terms of all performance measures we consider. Applying stabJGL to proteomic data from a pan-cancer study, we demonstrate the potential for novel discoveries the method brings. A user-friendly R package for stabJGL with tutorials is available on Github at https://github.com/Camiling/stabJGL.
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。
