标题： 稀疏网络的推理具有未观测变量。 应用于基因调控网络。 显示英文标题

标题： Inference of Sparse Networks with Unobserved Variables. Application to Gene Regulatory Networks

摘要： 网络是一种用于建模复杂系统的统一框架，网络推理问题在许多领域经常遇到。在这里，我开发并应用了一种生成方法来解决网络推理问题（RCweb），适用于网络稀疏且潜在（未观察到的）变量影响观测变量的情况。从所有可能的因素分析（FA）分解中，RCweb选择与稀疏底层网络一致的FA分解。稀疏性约束通过一种新方法施加，该方法在准确性、抗噪声鲁棒性、复杂度缩放和计算效率方面显著优于贝叶斯方法以及使用l1范数松弛的MLE方法，如K-SVD和基于l1的稀疏主成分分析（PCA）。模拟模型的结果表明，RCweb能够精确恢复稀疏度低至50%（即非稀疏）的模型结构，并且当未观测变量与观测变量的比例高达2时也能如此。RCweb对噪声具有鲁棒性，在噪声水平增加时参数范围逐渐减小。 显示英文摘要

摘要： Networks are a unifying framework for modeling complex systems and network inference problems are frequently encountered in many fields. Here, I develop and apply a generative approach to network inference (RCweb) for the case when the network is sparse and the latent (not observed) variables affect the observed ones. From all possible factor analysis (FA) decompositions explaining the variance in the data, RCweb selects the FA decomposition that is consistent with a sparse underlying network. The sparsity constraint is imposed by a novel method that significantly outperforms (in terms of accuracy, robustness to noise, complexity scaling, and computational efficiency) Bayesian methods and MLE methods using l1 norm relaxation such as K-SVD and l1--based sparse principle component analysis (PCA). Results from simulated models demonstrate that RCweb recovers exactly the model structures for sparsity as low (as non-sparse) as 50% and with ratio of unobserved to observed variables as high as 2. RCweb is robust to noise, with gradual decrease in the parameter ranges as the noise level increases.