陕西师范大学学报（自然科学版）2024，Vol.52Issue(3)：63-75,13.DOI:10.15983/j.cnki.jsnu.2024010
基于基因注意力和多组学的低级别胶质瘤分类方法
A classification method for low-grade glioma based on gene attention and multi-omics
摘要
Abstract
Existing studies on the three-class classification of molecular subtypes of low-grade glioma(LGG)rely on LGG medical imaging data.The scarcity and difficulty of obtaining data samples make it challenging for models to learn the differences between LGG molecular subtypes,reducing the model's classification performance.A three-class classification method for LGG molecular subtypes called MODDA is proposed,which utilizes a gene attention network to extract important features from LGG multi-omics data and employs an embedding network to process clinical data to obtain clinical data features.Then fuses clinical data features with important omics data features and uses a dense deep neural network for the classification of LGG molecular subtypes.Experimental results show that MODDA's classification performance surpasses existing LGG molecular subtype classification methods and also exhibits good generalization performance on external validation datasets.Moreover,an enrichment analysis of important genes identified during the chi-square testing process for gene ontology(GO)terms and biological pathways is conducted,aiding in the personalized treatment of LGG.关键词
低级别胶质瘤/分子亚型/多组学数据/基因注意力/深度神经网络Key words
low-grade glioma/molecular subtypes/multi-omics data/gene attention/deep neural network分类
信息技术与安全科学引用本文复制引用
程昊,韩笑,任建雪,闫奥煜,王会青..基于基因注意力和多组学的低级别胶质瘤分类方法[J].陕西师范大学学报（自然科学版）,2024,52(3):63-75,13.基金项目
山西省自然科学基金(202203021211121) （202203021211121）
