Predicting the Survival of Cancer Patients With Multimodal Graph Neural Network
Predicting the Survival of Cancer Patients With Multimodal Graph Neural Network
复制标题
用多模态图神经网络预测癌症患者的生存期
DOI：
10.1109/tcbb.2021.3083566
复制
发表时间：
2022-03-01
影响因子：
4.5
通讯作者：
Li, Zhao
中科院分区：
文献类型：
--
作者：
Gao, Jianliang;Lyu, Tengfei;Li, Zhao
近年来，癌症患者的生存预测对世界范围内的健康问题具有重要意义，并受到医学信息界许多研究者的关注。可见肿瘤患者生存预测的分类工作是一项有意义和挑战性的工作。然而，这一领域的研究仍然有限。在这项工作中，我们设计了一个新的多模态图神经网络（MGNN）框架来预测癌症生存率，它在一个统一的框架中探索了现实世界中多模态数据的特征，如基因表达，拷贝数改变和临床数据。具体而言，我们首先构建患者和多模态数据之间的二分图，以探索其内在联系。然后，利用图神经网络得到每个患者在不同二分图上的嵌入。最后，提出了一个多模态融合神经层，用于融合来自不同模态数据的医学特征。在真实世界的数据集上进行了全面的实验，证明了我们的模型的优越性，与最先进的模型相比有了显着的改进。此外，所提出的MGNN在其他四个癌症数据集上被验证为更鲁棒。
In recent years, cancer patients survival prediction holds important significance for worldwide health problems, and has gained many researchers attention in medical information communities. Cancer patients survival prediction can be seen the classification work which is a meaningful and challenging task. Nevertheless, research in this field is still limited. In this work, we design a novel Multimodal Graph Neural Network (MGNN)framework for predicting cancer survival, which explores the features of real-world multimodal data such as gene expression, copy number alteration and clinical data in a unified framework. Specifically, we first construct the bipartite graphs between patients and multimodal data to explore the inherent relation. Subsequently, the embedding of each patient on different bipartite graphs is obtained with graph neural network. Finally, a multimodal fusion neural layer is proposed to fuse the medical features from different modality data. Comprehensive experiments have been conducted on real-world datasets, which demonstrate the superiority of our modal with significant improvements against state-of-the-arts. Furthermore, the proposed MGNN is validated to be more robust on other four cancer datasets.