Bayesian inference for duplication-mutation with complementarity network models.
Bayesian inference for duplication-mutation with complementarity network models.
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DOI：
10.1089/cmb.2015.0072
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发表时间：
2015-11
期刊：
影响因子：
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通讯作者：
De Iorio M
中科院分区：
文献类型：
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作者：
Jasra A;Persing A;Beskos A;Heine K;De Iorio M
我们观察到一个没有多条边和自环的无向图G，它代表了一个蛋白质-蛋白质相互作用(PPI)网络。我们假设G是在种子图G0的复制突变互补模型下进化的，我们还观察到了代表G的复制历史的二进制森林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理的采样策略；该采样策略采用了粒子边际Metropolis-Hastings算法。我们在数值例子上测试了我们的方法，以证明我们的方法在推断DMC模型的突变和同质二聚化参数方面具有很高的准确性和精确度。
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.