基于基因注意力和多组学的低级别胶质瘤分类方法OA北大核心CSTPCD

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.