论文标题
使用马尔可夫随机场模型推断混合图形模型用于二分法表型
Inference of Mixed Graphical Models for Dichotomous Phenotypes using Markov Random Field Model
论文作者
论文摘要
在本文中，我们提出了一种名为Fused混合图形模型（FMGM）的新方法，该方法可以推断二分法表型的网络结构。我们假设不同的OMICS标记的相互作用与疾病状态有关，并提出了一种基于FMGM的方法来检测相关的OMICS标记网络差异。网络的统计模型基于成对的马尔可夫随机场模型，并添加惩罚函数以最大程度地减少网络中稀疏性的影响。快速近端梯度法（PGM）用于优化目标函数。使用模拟幂律网络结构的合成数据集测量方法有效性，发现FMGM表现出较高的性能，尤其是在F1得分方面，与先前的方法相比，依次推断网络（0.392和0.546）。 FMGM不仅在识别差异（0.217和0.410），而且在识别网络（0.492和0.572）方面表现更好。该提出的方法应用于有或没有特应特应性皮炎（AD）的6个月大的婴儿的多摩学特征，并且发现与类胡萝卜素生物合成相关的微生物基因与疾病状态降解的微生物基因之间存在不同的相关性，这表明与氧化应激和微生物含量相关的重要性。
In this article, we propose a new method named fused mixed graphical model (FMGM), which can infer network structures for dichotomous phenotypes. We assumed that the interplay of different omics markers is associated with disease status and proposed an FMGM-based method to detect the associated omics marker network difference. The statistical models of the networks were based on a pairwise Markov random field model, and penalty functions were added to minimize the effect of sparseness in the networks. The fast proximal gradient method (PGM) was used to optimize the target function. Method validity was measured using synthetic datasets that simulate power-law network structures, and it was found that FMGM showed superior performance, especially in terms of F1 scores, compared with the previous method inferring the networks sequentially (0.392 and 0.546). FMGM performed better not only in identifying the differences (0.217 and 0.410) but also in identifying the networks (0.492 and 0.572). The proposed method was applied to multi-omics profiles of 6-month-old infants with and without atopic dermatitis (AD), and different correlations were found between the abundance of microbial genes related to carotenoid biosynthesis and RNA degradation according to disease status, suggesting the importance of metabolism related to oxidative stress and microbial RNA balance.
