Abstract
The prediction of essential proteins in protein-protein interaction (PPI) networks plays a pivotal part in improving the cognition of biological organisms. This study presents a novel computational technique, called EPIA, to discover essential proteins by employing immune algorithm. In EPIA, each antibody denotes a candidate essential protein set, which is initialized in a random way among all proteins in a PPI network. Then the vaccine is extracted based on the prediction results of the existing essential protein identification methods. Next, EPIA utilizes four operators, crossover, mutation, vaccination and immune selection to update the antibody population and search for the optimal candidate essential protein set. The experimental results on two species (Saccharomyces cerevisiae and Drosophila melanogaster) demonstrate that EPIA can obtain a better performance on identifying essential proteins compared to other existing methods.
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