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
程昊 1韩笑 1任建雪 1闫奥煜 1王会青1
作者信息
- 1. 太原理工大学 计算机科学与技术学院(大数据学院),山西 太原 030600
- 折叠
摘要
现有对低级别胶质瘤(low-grade glioma,LGG)分子亚型三分类的研究依赖于 LGG医学影像数据,数据样本少且难获取导致模型较难学习到 LGG分子亚型之间的差异,降低了模型的分类性能.基于此,提出了LGG分子亚型三分类方法 MODDA,利用基因注意力网络提取 LGG多组学数据的重要特征,使用嵌入网络处理临床数据得到临床数据特征;将临床数据特征与组学数据重要特征进行融合,采用密集深度神经网络进行 LGG分子亚型分类.实验结果表明,MODDA的分类性能优于现有 LGG分子亚型分类方法,并且在外部验证数据集上也表现出较好的泛化性能.此外,对卡方检验过程中发现的重要基因进行了富集基因本体论(gene ontology,GO)术语和生物学途径分析,有助于LGG的个性化治疗.
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引用本文复制引用
基金项目
山西省自然科学基金(202203021211121)
出版年
2024
国家科技期刊平台
NSTL
维普
万方数据