多模态深度神经网络的高级别浆液性卵巢癌分类方法

Classification of High-Grade Serous Ovarian Cancer by Multi-Modal Deep Neural Networks

提出了高级别浆液性卵巢癌(HGSOC)分子亚型分类模型MMDNN-HGSOC,该模型将miRNA表达、DNA甲基化、拷贝数变异(CNV)与mRNA表达数据进行集成,构建多组学特征空间;基于LASSO(Least Absolute Shrinkage and Selection Operator)回归算法,提出叠加式LASSO(S-LASSO)回归算法,充分获得每个组学数据中与HGSOC分子亚型关联的基因子集;引入多组学数据晚期集成策略,利用多模态深度神经网络学习不同组学数据的高级特征表示。实验结果表明,MMDNN-HGSOC在HGSOC分子亚型分类中表现出较好性能。此外,对特征选择过程中发现的重要基因进行了GO(Gene Ontology)和KEGG(Kyoto Encycloped Genomes)富集分析,为HGSOC分子亚型鉴定和发病机制的研究提供有力支持。

A molecular subtype classification model MMDNN-HGSOC is proposed,which integrates miRNA expression,DNA methylation,and copy number variation(CNV)with mRNA expression data to construct a multiomics feature space.Based on LASSO(Least Absolute Shrinkage and Selection Operator)regression algorithm,a superposed LASSO(S-LASSO)regression algorithm is proposed to fully obtain gene subsets associated with HGSOC subtypes in each omics data.A late integration strategy for multi-omics data is introduced,and multi-modal deep neural networks are used to learn advanced feature representations of different omics data.The experimental results show that MMDNN-HGSOC performs well in the classification of HGSOC molecular subtypes.In addition,GO(Gene Ontology)and KEGG(Kyoto Encycloped Genomes)enrichment analyses are conducted on important genes discovered during feature selection,providing a strong support for the molecular subtype identification and pathogenesis research of HGSOC.