Conference proceeding
MGNN: A Multimodal Graph Neural Network for Predicting the Survival of Cancer Patients
Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR '20), pp 1697-1700
01 Jan 2020
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
Predicting the survival of cancer patients holds significant meaning for public health, and has attracted increasing attention in medical information communities. In this study, we propose a novel framework for cancer survival prediction named Multimodal Graph Neural Network (MGNN), which explores the features of real-world multimodal data such as gene expression, copy number alteration and clinical data in a unified framework. In order to explore the inherent relation, we first construct the bipartite graphs between patients and multimodal data. Subsequently, graph neural network is adopted to obtain the embedding of each patient on different bipartite graphs. Finally, a multimodal fusion neural layer is designed to fuse the features from different modal data. The output of our method is the classification of short term survival or long term survival for each patient. Experimental results on one breast cancer dataset demonstrate that MGNN outperforms all baselines. Furthermore, we test the trained model on lung cancer dataset, and the experimental results verify the strong robust by comparing with state-of-the-art methods.
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Details
- Title
- MGNN: A Multimodal Graph Neural Network for Predicting the Survival of Cancer Patients
- Creators
- Jianliang Gao - Central South UniversityTengfei Lyu - Central South UniversityFan Xiong - Central South UniversityJianxin Wang - Central South UniversityWeimao Ke - Drexel UniversityZhao Li - Alibaba Group (China)
- Publication Details
- Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR '20), pp 1697-1700
- Conference
- SIGIR '20: 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval, 43rd (Virtual Event China, 25 Jul 2020–30 Jul 2020)
- Publisher
- ACM Assoc Computing Machinery
- Number of pages
- 4
- Grant note
- 61873288,61836016 / National Natural Science Foundation of China; National Natural Science Foundation of China (NSFC)
- Resource Type
- Conference proceeding
- Language
- English
- Academic Unit
- Information Science
- Web of Science ID
- WOS:000722377700205
- Scopus ID
- 2-s2.0-85090163137
- Other Identifier
- 991019167672304721
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- Collaboration types
- Industry collaboration
- Domestic collaboration
- International collaboration
- Web of Science research areas
- Computer Science, Information Systems