Abstract
Complex network approach allows the representation and analysis of complex systems of interacting agents in an ordered and effective manner, thus increasing the probability of discovering significant properties of them. In the present study, we defined and built for the first time a complex network based on data obtained from Immune Epitope Database for parasitic organisms. We then considered the general topology, the node degree distribution, and the local structure (triadic census) of this network. In addition, we calculated 9 node centrality measures for observed network and reported a comparative study of the real network with three theoretical models to detect similarities or deviations from these ideal networks. The results obtained corroborate the utility of the complex network approach for handling information and data mining within the database under study. They confirm that this type of approach can be considered a valuable tool for preliminary screening of the best experimental conditions to determine whether the amino acid sequences being studied are true epitopes or not.
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