Abstract:
: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.
journal_name
Ann Lab Medjournal_title
Annals of laboratory medicineauthors
Lee N,Lee H,Moon SY,Sohn JY,Hwang SM,Yoon OJ,Youn HS,Eom HS,Kong SYdoi
10.3343/alm.2015.35.6.563subject
Has Abstractpub_date
2015-11-01 00:00:00pages
563-9issue
6eissn
2234-3806issn
2234-3814pii
201511563journal_volume
35pub_type
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