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- W4313431324 abstract "Abstract Accurate recognition of protein complexes is of great practical significance for biological process mechanism research, disease dynamic analysis, target spot based drug design and other applications. However, the existing recognition algorithms still lack the ability to represent the information of changing nodes and edges in dynamic protein networks, which greatly affects the accuracy of recognition. Considering the great advantages of graph convolution neural network in processing graph data, a dynamic protein network complex recognition algorithm based on spatial-temporal graph convolution is proposed. Firstly, the edge strength, node strength and edge existence probability are defined to model the dynamic protein network. Then, combined with the time series information and structure information on the graph, two convolution operators are designed based on Hilbert Huang transform, attention mechanism and residual connection technology to represent and learn the characteristics of proteins in the network, and the dynamic protein network characteristic map was constructed. Finally, spectral clustering is used to identify protein complexes, which well fits the development and change law of complexes in dynamic network environment. The experimental results on several public biological data sets show that the performance of the proposed algorithm is better than the latest complex recognition methods in recall, precision, coverage, function enrichment, reliability and efficiency." @default.
- W4313431324 created "2023-01-06" @default.
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- W4313431324 date "2022-12-20" @default.
- W4313431324 modified "2023-10-11" @default.
- W4313431324 title "A New Protein Complex Recognition Algorithm Based on Spatial-Temporal Graph Convolution" @default.
- W4313431324 doi "https://doi.org/10.21203/rs.3.rs-2335641/v1" @default.
- W4313431324 hasPublicationYear "2022" @default.
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