基于堆叠降噪自编码器的肝癌亚型分类  

CLASSIFICATION OF LIVER CANCER SUBTYPES BASED ON STACKED DENOISING AUTOENCODER

作　　者：张甜甜 赵庶旭[1] 王小龙[1] Zhang Tiantian;Zhao Shuxu;Wang Xiaolong(School of Electronic and Information Engineering,Lanzhou Jiaotong University,Lanzhou 730070,Gansu,China)

机构地区：[1]兰州交通大学电子与信息工程学院,甘肃兰州730070

出　　处：《计算机应用与软件》2024年第6期79-84,共6页Computer Applications and Software

基　　金：国家自然科学基金项目(6206020135)。

摘　　要：肝癌是威胁人类健康的常见恶性肿瘤之一。通过对基因数据使用深度学习方法进行整合来系统地获取对肝癌的认知,使用多组学的疾病分析方法来探究各组学之间的相互关系,有助于更准确的临床决策。然而,由于多组学数据具有高维稀疏性,存在大量的冗余特征和较少的可用临床标签样本。堆叠降噪编码器(SDAE)是能够从海量数据中获取有效特征的高效模型,因此基于SDAE模型提出一种层次式堆叠降噪编码器,来学习肝癌的RNA表达、miRNA表达和DNA甲基化数据的特征并进行整合和识别。实验结果表明:Hi-SDAE方法提高了对肝癌亚型分类的准确度,为肝癌针对性治疗提供了更有价值的参考依据。Liver cancer is a common malignant tumor that threatens human health.To systematically acquire the knowledge of liver cancer by integrating genetic data using deep learning methods,we use a multi-omics disease analysis approach to explore the interrelationships between the groups and to obtain more accurate clinical decisions.However,due to the high dimensional sparsity of multi-omics data,there are a large number of redundant features and fewer available clinical label samples.Stacked denoising autoencoder(SDAE)is an efficient model that can obtain effective features from massive data.Therefore,based on the SDAE model,a hierarchical stacking denoising encoder was proposed to learn and integrate the characteristics of RNA expression,miRNA expression and DNA methylation data of liver cancer.The results show that the Hi-SDAE method improves the accuracy of the classification of liver cancer subtypes,and provides a more valuable reference for the targeted treatment of liver cancer.

关 键 词：堆叠降噪 自动编码器 数据降维 多组学整合 肝癌亚型 

分 类 号：TP311.13[自动化与计算机技术—计算机软件与理论] R730.2[自动化与计算机技术—计算机科学与技术]