基于图神经网络的中药系统生物学信息挖掘算法研究

Research on the Algorithm of Mining Information of Traditional Chinese Herb System Biology Based on Graph Neural Network

  • 摘要:
    目的 构建中药-基因-蛋白复杂网络,优化中药潜在关联基因的挖掘方法,提升中药系统生物学信息的挖掘效能,为进一步探究中药作用机制提供帮助。
    方法 提出融合注意力机制的图神经网络模型HERBGAT,以公开数据平台中少量的中药关联基因数据为输入,在中药-基因-蛋白复杂网络中进行深度挖掘,输出潜在的中药关联基因,将预测结果通过生信平台进行Disease关联分析、KEGG信号通路分析阐明其作用机制,并借助文献检索平台进行预测结果验证。
    结果 训练结果表明,HERBGAT模型预测准确率均值可达94%,相较于其他2种先进的复杂网络挖掘方法,HERBGAT在ACC、AUC和AUPR三项指标中均表现出更优秀的性能;在文献验证环节,模型预测结果得到中医临床文献及现代药理学文献证明,展现出HERBGAT在实际应用中的良好效果。最后,以借助HERBGAT模型和改进的EMOGI模型探究半夏治疗肺癌作用机制为例,发现半夏治疗肺癌的潜在关联基因199个,并借助生物信息学方法对这些潜在关联基因进行初步分析探讨。
    结论 HERBGAT模型能有效挖掘潜在的中药关联基因,提高中药-基因-蛋白复杂网络的挖掘效能,为中药系统生物学信息挖掘方法的优化提供新的思路与参考,为探究中药作用机制等研究提供数据基础及实验方向。

     

    Abstract:
    OBJECTIVE To provide help for further exploring the mechanism of action of traditional Chinese herb by constructing a complex network of traditional Chinese herb-gene-protein, optimizing the mining method of potential associated genes of traditional Chinese herb and improving the mining efficiency of traditional Chinese herb system biology information.
    METHODS A graph neural network model HERBGAT with an attention mechanism was proposed. A small amount of traditional Chinese herb-related gene data in the public data platform was used as input, and deep mining was performed in the traditional Chinese herb-gene-protein complex network to output potential traditional Chinese herb-related genes. The prediction results were analyzed by disease association analysis and KEGG signaling pathway analysis on the bioinformatics platform to clarify their mechanism of action, and the prediction results were verified by the literature retrieval platform.
    RESULTS The training results showed that the average prediction accuracy of the HERBGAT model could reach 94%. Compared with the other two advanced complex network mining methods, HERBGAT showed better performance in the three indicators of ACC, AUC and AUPR. In the literature verification stage, the model prediction results were verified by TCM clinical literature and modern pharmacology literature, showing the good effect of HERBGAT in practical application. At the end of this paper, taking the HERBGAT model and the improved EMOGI model to explore the mechanism of action of Pinellia ternata in treating lung cancer as an example, 199 potential associated genes of Pinellia ternata in treating lung cancer were found, and these potential associated genes were preliminarily analyzed and discussed with the help of bioinformatics methods.
    CONCLUSION The HERBGAT model can effectively mine potential traditional Chinese herb-associated genes, improve the mining efficiency of traditional Chinese herb-gene-protein complex networks, provide new ideas and references for the optimization of traditional Chinese herb system biology information mining methods, and provide data basis and experimental direction for exploring the mechanism of action of traditional Chinese herb.

     

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