DMGNN: Cancer driver gene identification based on mix-moment graph neural network

Bolin Chen, Ziyuan Li, Haodong Li, Junming Li, Ruiming Kang, Xuequn Shang, Jun Bian

科研成果: 书/报告/会议事项章节会议稿件同行评审

摘要

Cancer development is closely linked to the accumulation of mutations in driver genes. Therefore, identification of driver genes is crucial for understanding the molecular basis of cancer. In various types of methods, approches based on Graph Neural Networks (GNNs) framework are one of the most effective tools to identify driver genes, which can also combine biological networks and multi-omics data to further improve the identification accuracy. However, many GNN frameworks often utilize single-order moment to get neighbourhood information for message passing, which ignores the rich distribution information and gene features of neighbouring genes. Besides, when using GNNs model, it is necessary to stack the hidden layers, which can often lead to both over-smoothing and network degradation problems. To overcome these issues, a new framework called DMGNN was proposed for identifying driver genes. To get rich distribution information and gene features, a mix-moment embedding and attention-based feature selection were utilized among neighbouring genes. To solve the problem posed by hidden layer stacking in the GNN model, a deepwalk method was used to learn remote gene effects for the over-smoothing problem, and the resNet-based hidden layer aggregation was employed to mitigate the network degradation issue. Experimental results demonstrate that the proposed model outperforms many existing methods for identifying cancer driver genes, where the AUROC value was achieved at 0.856 in STRING dataset with at least 2 percentage improvement comparing with other models. The DMGNN is freely available via https://github.com/lavendar682/DMGNN.

源语言英语
主期刊名Proceedings - 2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024
编辑Mario Cannataro, Huiru Zheng, Lin Gao, Jianlin Cheng, Joao Luis de Miranda, Ester Zumpano, Xiaohua Hu, Young-Rae Cho, Taesung Park
出版商Institute of Electrical and Electronics Engineers Inc.
6240-6247
页数8
ISBN(电子版)9798350386226
DOI
出版状态已出版 - 2024
活动2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024 - Lisbon, 葡萄牙
期限: 3 12月 20246 12月 2024

出版系列

姓名Proceedings - 2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

会议

会议2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024
国家/地区葡萄牙
Lisbon
时期3/12/246/12/24

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