Jasra Ajay, Persing Adam, Beskos Alexandros, Heine Kari, De Iorio Maria
1 Department of Statistics & Applied Probability, National University of Singapore , Singapore, Singapore .
2 Department of Statistical Science, University College London , London, United Kingdom .
J Comput Biol. 2015 Nov;22(11):1025-33. doi: 10.1089/cmb.2015.0072. Epub 2015 Sep 10.
We observe an undirected graph G without multiple edges and self-loops, which is to represent a protein-protein interaction (PPI) network. We assume that G evolved under the duplication-mutation with complementarity (DMC) model from a seed graph, G0, and we also observe the binary forest Γ that represents the duplication history of G. A posterior density for the DMC model parameters is established, and we outline a sampling strategy by which one can perform Bayesian inference; that sampling strategy employs a particle marginal Metropolis-Hastings (PMMH) algorithm. We test our methodology on numerical examples to demonstrate a high accuracy and precision in the inference of the DMC model's mutation and homodimerization parameters.
我们观察一个无向图G,它没有多重边和自环,用于表示蛋白质-蛋白质相互作用(PPI)网络。我们假设G在具有互补性的复制-突变(DMC)模型下从种子图G0演化而来,并且我们还观察到表示G的复制历史的二叉森林Γ。建立了DMC模型参数的后验密度,并概述了一种可以进行贝叶斯推断的采样策略;该采样策略采用粒子边缘Metropolis-Hastings(PMMH)算法。我们在数值示例上测试我们的方法,以证明在推断DMC模型的突变和同二聚化参数时具有高精度和高精确度。