具有潜变量的生物网络循环图模型的结构可辨识性
Structural Identifiability of Cyclic Graphical Models of Biological Networks with Latent Variables
摘要：
本文提出了一种适用于多种网络结构的结构可识别性分析算法。该方法采用Wright路径系数法生成符号多项式形式的可辨识性方程，然后将这些符号方程转化为二元矩阵（称为可辨识性矩阵）。在保持系统等价性的前提下，引入了几种矩阵运算，实现了可辨识矩阵的降阶。基于约简的可辨识性矩阵，确定了各参数的结构可辨识性。一些基准模型被用来验证所提出的方法的有效性。最后，以甲型流感病毒复制网络模型为例，说明了该方法在实际中的应用。所提出的方法可以处理具有潜在变量的循环网络。关键的优点是它有意避免了符号计算，因此效率很高。此外，这种方法是能够确定每个单一的参数的可识别性，因此与许多现有的方法相比，具有更高的分辨率。总体而言，这项研究提供了一个基础，从可识别性的角度来看，生物网络的图形模型的系统检查和完善，它有一个显着的潜力被扩展到更复杂的网络结构或高维系统。
英文摘要：
An efficient structural identifiability analysis algorithm is developed in this study for a broad range of network structures. The proposed method adopts the Wright's path coefficient method to generate identifiability equations in forms of symbolic polynomials, and then converts these symbolic equations to binary matrices (called identifiability matrix). Several matrix operations are introduced for identifiability matrix reduction with system equivalency maintained. Based on the reduced identifiability matrices, the structural identifiability of each parameter is determined. A number of benchmark models are used to verify the validity of the proposed approach. Finally, the network module for influenza A virus replication is employed as a real example to illustrate the application of the proposed approach in practice. The proposed approach can deal with cyclic networks with latent variables. The key advantage is that it intentionally avoids symbolic computation and is thus highly efficient. Also, this method is capable of determining the identifiability of each single parameter and is thus of higher resolution in comparison with many existing approaches. Overall, this study provides a basis for systematic examination and refinement of graphical models of biological networks from the identifiability point of view, and it has a significant potential to be extended to more complex network structures or high-dimensional systems.
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。
