J Sheng1,2, J Xue3, P Li4, N Yi4. 1. Clinical nursing teaching and Research Office, The Second Xiangya Hospital of Central South University, Changsha 410011, China. 2. Department of ultrasound diagnosis, The Second Xiangya Hospital of Central South University, Changsha 410011, China. 3. Operation center, The Third Xiangya Hospital of Central South University, Changsha 410013, China. 4. School of Informatics, Hunan University of Chinese Medicine, Changsha 410208, China.
Abstract
OBJECTIVE: To propose a new method for mining complexes in dynamic protein network using spatiotemporal convolution neural network. METHODS: The edge strength, node strength and edge existence probability are defined for modeling of the dynamic protein network. Based on the time series information and structure information on the graph, two convolution operators were designed using Hilbert-Huang transform, attention mechanism and residual connection technology to represent and learn the characteristics of the proteins in the network, and the dynamic protein network characteristic map was constructed. Finally, spectral clustering was used to identify the protein complexes. RESULTS: The simulation results on several public biological datasets showed that the F value of the proposed algorithm exceeded 90% on DIP dataset and MIPS dataset. Compared with 4 other recognition algorithms (DPCMNE, GE-CFI, VGAE and NOCD), the proposed algorithm improved the recognition efficiency by 34.5%, 28.7%, 25.4% and 17.6%, respectively. CONCLUSION: The application of deep learning technology can improve the efficiency in analysis of dynamic protein networks.
OBJECTIVE: To propose a new method for mining complexes in dynamic protein network using spatiotemporal convolution neural network. METHODS: The edge strength, node strength and edge existence probability are defined for modeling of the dynamic protein network. Based on the time series information and structure information on the graph, two convolution operators were designed using Hilbert-Huang transform, attention mechanism and residual connection technology to represent and learn the characteristics of the proteins in the network, and the dynamic protein network characteristic map was constructed. Finally, spectral clustering was used to identify the protein complexes. RESULTS: The simulation results on several public biological datasets showed that the F value of the proposed algorithm exceeded 90% on DIP dataset and MIPS dataset. Compared with 4 other recognition algorithms (DPCMNE, GE-CFI, VGAE and NOCD), the proposed algorithm improved the recognition efficiency by 34.5%, 28.7%, 25.4% and 17.6%, respectively. CONCLUSION: The application of deep learning technology can improve the efficiency in analysis of dynamic protein networks.
Entities:
Keywords:
convolution operator; dynamic protein network; graph convolution neural network; protein complex; spectral clustering