Bayesian inference for duplication-mutation with complementarity network models.
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.
1 Department of Statistics & Applied Probability, National University of Singapore , Singapore, Singapore .
Ajay
A
Jasra
2 Department of Statistical Science, University College London , London, United Kingdom .
Adam
A
Persing
2 Department of Statistical Science, University College London , London, United Kingdom .
Alexandros
A
Beskos
2 Department of Statistical Science, University College London , London, United Kingdom .
Kari
K
Heine
2 Department of Statistical Science, University College London , London, United Kingdom .
Maria
M
De Iorio
D000465:Algorithms; D001499:Bayes Theorem; D020440:Gene Duplication; D008390:Markov Chains; D008957:Models, Genetic; D009010:Monte Carlo Method; D060066:Protein Interaction Maps
duplication–mutation with complementarity (DMC) model; particle marginal Metropolis–Hastings (PMMH); protein–protein interaction (PPI) network; sequential Monte Carlo (SMC)