The biological organism is a complex structure regulated by interactions of genes and proteins. Various linear and nonlinear models can define activations of these interactions. In this study, we have aimed to improve the Gaussian graphical model (GGM), which is one of the well-known probabilistic and parametric models describing steady-state activations of biological systems, and its inference based on the graphical lasso, shortly Glasso, method. Because, GGM with Glasso can have low accuracy when the system has many genes and data are far from the normal distribution. Hereby, we construct the model like GGM, but, suggest the long-tailed symmetric distribution (LTS), rather than the normality, and use the modified maximum likelihood (MML) estimator, rather than Glasso, in inference. From the assessment of simulated and real data analyses, it is seen that LTS with MML has higher accuracy and less computational demand with explicit expressions than results of GGM with Glasso.

中文翻译：

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生物网络模型参数的长尾图模型和频率论推理

生物有机体是由基因和蛋白质相互作用调节的复杂结构。各种线性和非线性模型可以定义这些相互作用的激活。在这项研究中，我们旨在改进高斯图形模型 (GGM)，它是描述生物系统稳态激活的众所周知的概率和参数模型之一，其基于图形套索的推断，简称为 Glasso，方法。因为，当系统有很多基因并且数据远离正态分布时，GGM with Glasso 的准确率会很低。因此，我们构建了类似于 GGM 的模型，但是，建议使用长尾对称分布 (LTS)，而不是正态分布，并在推理中使用修改后的最大似然 (MML) 估计量，而不是 Glasso。从模拟和真实数据分析的评估中，