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Multiplex Networks and Pan-Cancer Multiomics-Based Driver Gene Identification Using Graph Neural Networks

School of Computer Science, Northwestern Polytechnical University, Xi’an 710072, China, and with Research & Development Institute of Northwestern Polytechnical University in Shenzhen, Shenzhen 518063, China
School of Software, Northwestern Polytechnical University, Xi’an 710072, China, and with Research & Development Institute of Northwestern Polytechnical University in Shenzhen, Shenzhen 518063, China
School of Computer Science, Northwestern Polytechnical University, Xi’an 710072, China
School of Computer Science and Engineering, Central South University, Changsha 410083, China
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Abstract

Identifying cancer driver genes has paramount significance in elucidating the intricate mechanisms underlying cancer development, progression, and therapeutic interventions. Abundant omics data and interactome networks provided by numerous extensive databases enable the application of graph deep learning techniques that incorporate network structures into the deep learning framework. However, most existing models primarily focus on individual network, inevitably neglecting the incompleteness and noise of interactions. Moreover, samples with imbalanced classes in driver gene identification hamper the performance of models. To address this, we propose a novel deep learning framework MMGN, which integrates multiplex networks and pan-cancer multiomics data using graph neural networks combined with negative sample inference to discover cancer driver genes, which not only enhances gene feature learning based on the mutual information and the consensus regularizer, but also achieves balanced class of positive and negative samples for model training. The reliability of MMGN has been verified by the Area Under the Receiver Operating Characteristic curves (AUROC) and the Area Under the Precision-Recall Curves (AUPRC). We believe MMGN has the potential to provide new prospects in precision oncology and may find broader applications in predicting biomarkers for other intricate diseases. Implementations of MMGN can be found at https://github.com/xingyili/MMGN.

Keywords

cancer driver genemultiplex networkspan-cancer multiomics datagraph neural networksnegative sample inference

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Big Data Mining and Analytics
Volume 7 Issue 4,
December 2024
Pages 1262-1272
DOI: 10.26599/BDMA.2024.9020043
Cite this article:
Li X, Li J, Hao J, et al. Multiplex Networks and Pan-Cancer Multiomics-Based Driver Gene Identification Using Graph Neural Networks. Big Data Mining and Analytics, 2024, 7(4): 1262-1272. https://doi.org/10.26599/BDMA.2024.9020043
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