Abstract
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, 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 existing state-of-the-art techniques. The source code and datasets of the IMA can be found at https://github.com/RongquanWang/IMA.
Highlights
Many complex systems in the real world are often modeled with complex networks, such as computer networks, social networks, and biological networks
We present a novel improved memetic algorithm (IMA) method for identifying protein complexes in protein-protein interaction (PPI) network
The key idea of IMA is enabled us to design an improved memetic algorithm to optimize a fitness function for identifying protein complexes in PPI networks based on existing contending methods and a weighted PPI network
Summary
Many complex systems in the real world are often modeled with complex networks, such as computer networks, social networks, and biological networks. Proteins rarely act alone, and often organize together to form protein complexes to perform specific biological functions cooperatively (Spirin and Mirny, 2003). Some experimental methods such as yeast two-hybrid and tandem affinity purification can detect protein complexes, they have limitations. Many computational methods have been proposed for the identification of protein complexes from PPI networks, which is a type of cluster analysis, Memetic Algorithm, Detecting Protein Complexes which consists of grouping patterns into clusters based on the similarity, and it is a valuable technology in many areas such as bioinformatics, machine learning, and computer vision
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