期刊
IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS
卷 12, 期 3, 页码 622-631出版社
IEEE COMPUTER SOC
DOI: 10.1109/TCBB.2014.2366748
关键词
Evolutionary models; protein interaction networks; differential evolution algorithm
类别
资金
- US National Institutes of Health [8P20GM103446-13]
Revealing the underlying evolutionary mechanism plays an important role in understanding protein interaction networks in the cell. While many evolutionary models have been proposed, the problem about applying these models to real network data, especially for differentiating which model can better describe evolutionary process for the observed network remains a challenge. The traditional way is to use a model with presumed parameters to generate a network, and then evaluate the fitness by summary statistics, which however cannot capture the complete network structures information and estimate parameter distribution. In this work, we developed a novel method based on Approximate Bayesian Computation and modified Differential Evolution algorithm (ABC-DEP) that is capable of conducting model selection and parameter estimation simultaneously and detecting the underlying evolutionary mechanisms for PPI networks more accurately. We tested our method for its power in differentiating models and estimating parameters on simulated data and found significant improvement in performance benchmark, as compared with a previous method. We further applied our method to real data of protein interaction networks in human and yeast. Our results show duplication attachment model as the predominant evolutionary mechanism for human PPI networks and Scale-Free model as the predominant mechanism for yeast PPI networks.
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