Abstract
Cancer is a highly heterogeneous disease, and cancer subtype diagnosis is a crucial stage in the whole process of cancer treatment. In recent years, the accumulation of multi-omics data has provided more complete data support for the diagnosis of cancer subtypes. However, the significant differences between different omics and their incorrect connections after composition remain significant obstacles to data fusion. The method based on multi view learning can effectively alleviate this problem. In this paper, we propose a novel Diagnosis of lung cancer subtypes by combining Multi-graph Embedding and Graph Fusion network (DMEGF). A graph fusion network is used to generate consensus graph of multi-omics data, and we use the multi-graph autoencoder with attention mechanism to learn the common representation. In addition, the similarity of adjacent features in common features is maintained according to the concept of mutual information. Then classify and recognize the comprehensive representations to diagnose cancer subtypes. The experiments on TCGA lung cancer datasets show that DMEGF has good diagnostic performance and good interpretability.
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This work has been supported by the National Natural Science Foundation of China (Grant No. 62032007).
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Peng, S., Luo, J., Shen, C., Wang, B. (2023). Diagnosis of Lung Cancer Subtypes by Combining Multi-graph Embedding and Graph Fusion Network. In: Huang, DS., Premaratne, P., Jin, B., Qu, B., Jo, KH., Hussain, A. (eds) Advanced Intelligent Computing Technology and Applications. ICIC 2023. Lecture Notes in Computer Science(), vol 14090. Springer, Singapore. https://doi.org/10.1007/978-981-99-4761-4_38
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DOI: https://doi.org/10.1007/978-981-99-4761-4_38
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