具有不可观测变量的稀疏网络的推理。基因调控网络的应用
Inference of Sparse Networks with Unobserved Variables. Application to Gene Regulatory Networks
摘要：
网络是复杂系统建模的统一框架，网络推理问题在许多领域中经常遇到。在这里，我开发并应用了一种生成式方法来进行网络推理（RCweb），用于网络稀疏且潜在（未观察到）变量影响观察到的变量的情况。从所有可能解释数据方差的因子分析（FA）分解中，RCweb选择与稀疏底层网络一致的FA分解。稀疏性约束是由一种新的方法，显着优于（在精度，鲁棒性噪声，复杂性缩放，和计算效率）贝叶斯方法和MLE方法使用L1范数松弛，如K-SVD和L1-基于稀疏主成分分析（PCA）。从模拟模型的结果表明，RCweb恢复精确的模型结构的稀疏性低（非稀疏）为50%，未观测到的观测变量的比例高达2。RCweb对噪声具有鲁棒性，随着噪声水平的增加，参数范围逐渐减小。
英文摘要：
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.
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。
