Identifying the protein complexes in protein-protein interaction (PPI) networks is essential for understanding cellular organization and biological processes.To address the high false positive/negative rates of PPI networks and detect protein complexes with multiple topological structures, we developed a novel improved memetic algorithm (IMA).IMA first combines the topological and biological properties to obtain a weighted PPI network with reduced noise.Next, it integrates various clustering results to construct the initial populations.
Furthermore, langify_image_container a fitness function is designed based on the five topological properties of the protein complexes.Finally, we describe the rest of our IMA method, which primarily consists of four steps: selection operator, recombination operator, local optimization strategy, and updating the population operator.In particular, IMA is a combination of genetic algorithm and a local optimization strategy, which has a strong global search ability, and searches for local optimal solutions effectively.The experimental results demonstrate that IMA performs much better than the base methods and Comforter Set existing state-of-the-art techniques.
The source code and datasets of the IMA can be found at https://github.com/RongquanWang/IMA.