Journal article
Predicting the Survival of Cancer Patients With Multimodal Graph Neural Network
IEEE/ACM transactions on computational biology and bioinformatics, v 19(2), pp 699-709
Mar 2022
PMID: 34033545
Abstract
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.
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Details
- Title
- Predicting the Survival of Cancer Patients With Multimodal Graph Neural Network
- Creators
- Jianliang Gao - Central South UniversityTengfei Lyu - Central South UniversityFan Xiong - Central South UniversityJianxin Wang - Central South UniversityWeimao Ke - Drexel UniversityZhao Li - Alibaba Group
- Publication Details
- IEEE/ACM transactions on computational biology and bioinformatics, v 19(2), pp 699-709
- Publisher
- IEEE
- Grant note
- 2021zzts0740 / Fundamental Research Funds for the Central Universities (10.13039/501100012226) CX20200108 / Hunan Provincial Innovation Foundation for Postgraduate (10.13039/501100010083) 2019KE0 AB01 / Zhejiang Lab 61873288; U1972423 / National Natural Science Foundation of China (10.13039/501100001809)
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Information Science
- Web of Science ID
- WOS:000777332300006
- Scopus ID
- 2-s2.0-85107180873
- Other Identifier
- 991019167450404721
InCites Highlights
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- Collaboration types
- Industry collaboration
- Domestic collaboration
- International collaboration
- Web of Science research areas
- Biochemical Research Methods
- Computer Science, Interdisciplinary Applications
- Mathematics, Interdisciplinary Applications
- Statistics & Probability