计算机科学
图形
解剖(医学)
人工神经网络
计算生物学
生物信息学
人工智能
医学
生物
理论计算机科学
解剖
作者
Hao Li,Zebei Han,Yu Sun,Fu Wang,Pengzhen Hu,Yuang Gao,Xuemei Bai,Shiyu Peng,Chao Ren,Xiang Xu,Zeyu Liu,Hebing Chen,Yang Yang,Xiaochen Bo
标识
DOI:10.1038/s41467-024-50426-6
摘要
Abstract Cancer is rarely the straightforward consequence of an abnormality in a single gene, but rather reflects a complex interplay of many genes, represented as gene modules. Here, we leverage the recent advances of model-agnostic interpretation approach and develop CGMega, an explainable and graph attention-based deep learning framework to perform cancer gene module dissection. CGMega outperforms current approaches in cancer gene prediction, and it provides a promising approach to integrate multi-omics information. We apply CGMega to breast cancer cell line and acute myeloid leukemia (AML) patients, and we uncover the high-order gene module formed by ErbB family and tumor factors NRG1 , PPM1A and DLG2 . We identify 396 candidate AML genes, and observe the enrichment of either known AML genes or candidate AML genes in a single gene module. We also identify patient-specific AML genes and associated gene modules. Together, these results indicate that CGMega can be used to dissect cancer gene modules, and provide high-order mechanistic insights into cancer development and heterogeneity.
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