Functional Module Mining Algorithms And Key Protein Recognition Methods In Protein Networks

Since the life science research is in the post-genomic era, an important content of proteomics is study and analysis the proteins by using information technology. Especially, the most important part is detecting protein functional modules and identifying essential proteins from protein-protein interaction networks, which can help to reveal the specific activities of organism.Because of the complex cell environment and the limitations of high-throughput techniques, the data in Protein-Protein Interaction (PPI) databases contains a lot of error messages. Furthermore, the protein interaction data can only show that the interaction was existed, but it cannot show when and where was it synthesized and resolved. The incomplete data has affected the accuracy of detecting protein functional modules from it. Essential protein is a kind of protein that plays an important role in organism's function. Maybe a miss of an essential protein can cause a miss of function. So identifying essential proteins have caused researchers'attention.The main studies of this paper are as follows:Firstly, this paper uses firework algorithm (FWA, for short) to optimize the expansion parameter of Markov clustering (MCL, for short) in order to detect functional modules in protein-protein interaction networks. There are two different processes, one is simulating explosions in the network, in this way, a firework and some special sparks can form a cluster, the other is use FWA as a part of MCL to optimize a parameter of it, the they can get results together. The experiments show that both of them can get a good performance.Secondly, this paper uses firefly optimization algorithm (FA, for short) to optimize the expansion parameter of MCL in order to detect functional modules in protein-protein interaction networks. FA is more effective than other swarm intelligence optimization algorithms, and the experiments illustrate this point. The experiments also show that it can get a better performance than other common used clustering algorithms.Thirdly, this paper proposes a method to identify essential proteins in functional modules, through analyzing the topological properties of functional modules. In functional modules, this paper uses degree centrality and edge clustering coefficient centralities to analyze the topological properties. The bigger a node's centralities, the more likely it will be an essential protein. The experiments show that this method can get more essential proteins in most samples.