基于图卷积神经网络的乳腺癌亚型分类研究

Identification of breast cancer subtypes based on graph convolutional network

恶性肿瘤分子亚型的准确识别是肿瘤患者个体化诊断、个性化治疗以及预后预测的重要支撑。综合性肿瘤基因组数据库的不断完善和深度学习技术的持续突破,推动了计算机辅助肿瘤分类技术的进一步发展。现有的基于基因表达数据的神经网络亚型分类方法虽然考虑了分子分型的复杂性,但仍然存在忽略基因内在关联性和协同性的问题。为了解决这一问题,本文提出了一种结合分层注意力机制的多层图卷积神经网络乳腺癌亚型分类模型。该模型基于先验的生物学知识构建乳腺癌患者的基因图表示数据集,训练出一种新的端到端的多分类模型,能够对乳腺癌分子亚型进行智能识别,并且在乳腺癌分子亚型的分类工作上表现出了很好的识别性能。相较于原始的图卷积神经网络以及两个主流的图神经网络分类算法,该模型在7分类任务中的准确率、加权F1分数、加权召回率、加权精确率分别达到了0.8517、0.8235、0.8517、0.7936,在4分类任务中则分别达到了0.9285、0.8949、0.9285、0.8650,具有明显的优势。此外,本文方法相较于最新的乳腺癌亚型分类算法,同样获得了最高的分类准确率。综上,本文所提模型或可作为一种辅助诊断技术为未来乳腺癌亚型的精确分类提供一个可信的选择,奠定计算机辅助肿瘤分类的理论基础。

Identification of molecular subtypes of malignant tumors plays a vital role in individualized diagnosis,personalized treatment,and prognosis prediction of cancer patients.The continuous improvement of comprehensive tumor genomics database and the ongoing breakthroughs in deep learning technology have driven further advancements in computer-aided tumor classification.Although the existing classification methods based on gene expression omnibus database take the complexity of cancer molecular classification into account,they ignore the internal correlation and synergism of genes.To solve this problem,we propose a multi-layer graph convolutional network model for breast cancer subtype classification combined with hierarchical attention network.This model constructs the graph embedding datasets of patients’genes,and develops a new end-to-end multi-classification model,which can effectively recognize molecular subtypes of breast cancer.A large number of test data prove the good performance of this new model in the classification of breast cancer subtypes.Compared to the original graph convolutional neural networks and two mainstream graph neural network classification algorithms,the new model has remarkable advantages.The accuracy,weight-F1-score,weight-recall,and weight-precision of our model in seven-category classification has reached 0.8517,0.8235,0.8517 and 0.7936 respectively.In the four-category classification,the results are 0.9285,0.8949,0.9285 and 0.8650 respectively.In addition,compared with the latest breast cancer subtype classification algorithms,the method proposed in this paper also achieved the highest classification accuracy.In summary,the model proposed in this paper may serve as an auxiliary diagnostic technology,providing a reliable option for precise classification of breast cancer subtypes in the future and laying the theoretical foundation for computer-aided tumor classification.