Bayesian Inference for Duplication-Mutation with Complementarity Network Models

Ajay Jasra; Adam Persing; Alexandros Beskos; Kari Heine; Maria De Iorio

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arXiv · 1504.01794

Bayesian Inference for Duplication-Mutation with Complementarity Network Models

Ajay Jasra·Adam Persing·Alexandros Beskos·Kari Heine·Maria De Iorio

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Abstract

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, $G_0$, and we also observe the binary forest $\Gamma$ 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.

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BibTeX RIS

Ajay Jasra, Adam Persing, Alexandros Beskos, Kari Heine, Maria De Iorio. 2015-04-08. Bayesian Inference for Duplication-Mutation with Complementarity Network Models. https://arxiv.org/abs/1504.01794

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