IEEE/ACM Trans Comput Biol Bioinform. 2022 Mar-Apr;19(2):699-709. doi: 10.1109/TCBB.2021.3083566. Epub 2022 Apr 1.
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.
近年来，癌症患者的生存预测对全球健康问题具有重要意义，已引起医学信息界众多研究人员的关注。癌症患者的生存预测可以看作是分类工作，这是一项有意义且具有挑战性的任务。然而，该领域的研究仍然有限。在这项工作中，我们设计了一种新颖的多模态图神经网络（MGNN）框架来预测癌症的生存情况，该框架在统一的框架中探索了真实世界多模态数据的特征，如基因表达、拷贝数改变和临床数据。具体来说，我们首先构建了患者和多模态数据之间的二分图，以探索其内在关系。随后，使用图神经网络获取不同二分图上每个患者的嵌入。最后，提出了一种多模态融合神经层来融合来自不同模态数据的医学特征。我们在真实数据集上进行了综合实验，结果表明，我们的模型具有显著的优势，相对于最先进的方法有了显著的改进。此外，所提出的 MGNN 在另外四个癌症数据集上的验证结果也表明其更稳健。
