A Protein Complex Identification Algorithm Based on essential protein In this paper, we develop a novel protein complex identification algorithm from the angle of point (SCMA). First, we select the protein with high degree and high connection strength as seed to form the preliminary cores. Then, the preliminary cores are filtered according to the density of complex core to obtain the unique core. Finally, the protein complexes are generated by identifying attachment proteins through second order connection strength for each core. We compare our algorithm by using OS (matching degree), Fmeasure, Coverage rate, Pvalue metrics with the existing algorithms. The results show that our algorithm is effective, it can identify protein complexes more accurately.

Identification of Essential Proteins Based on Centrality Methods Using Improved Collective Influence Algorithm Collective influence (CI) algorithm based on degree centrality is a very efficient method in the search of vital nodes in the large complex social networks. In this paper, we propose a novel CI method, called CIX frame and employ it to identify essential proteins in proteinprotein interaction (PPI) networks. More concretely, we apply the improved CI algorithm to process some stateoftheart methods which are based on centrality methods, such as DC, BC, CC, EC, SC and NC and so on. To investigate the performance of the CIX frame, some comparison experiments are implemented on the benchmark of two different yeast PPI networks obtained from DIP database and BioGRID database. The simulation results show that the identification accuracy of essential proteins of the most classical centrality methods can be improved by using our proposed CIX frame. It gives us a new insight to study the identification of essential proteins.