基于时空图卷积神经网络的蛋白质复合物识别方法
[A protein complex recognition method based on spatial-temporal graph convolution neural network].
作者信息
Sheng J, Xue J, Li P, Yi N
机构信息
Clinical nursing teaching and Research Office, The Second Xiangya Hospital of Central South University, Changsha 410011, China.
Department of ultrasound diagnosis, The Second Xiangya Hospital of Central South University, Changsha 410011, China.
出版信息
Nan Fang Yi Ke Da Xue Xue Bao. 2022 Jul 20;42(7):1075-1081. doi: 10.12122/j.issn.1673-4254.2022.07.17.
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.
目的
提出一种使用时空卷积神经网络在动态蛋白质网络中挖掘复合物的新方法。
方法
定义边强度、节点强度和边存在概率以对动态蛋白质网络进行建模。基于图上的时间序列信息和结构信息，利用希尔伯特-黄变换、注意力机制和残差连接技术设计了两个卷积算子，以表示和学习网络中蛋白质的特征，并构建动态蛋白质网络特征图。最后，使用谱聚类来识别蛋白质复合物。
结果
在几个公共生物数据集上的模拟结果表明，所提算法在DIP数据集和MIPS数据集上的F值超过90%。与其他4种识别算法（DPCMNE、GE-CFI、VGAE和NOCD）相比，所提算法的识别效率分别提高了34.5%、28.7%、25.4%和17.6%。
结论
深度学习技术的应用可以提高动态蛋白质网络的分析效率。
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