基于质谱数据的生成对抗自编码器整合投票算法  

Generative adversarial autoencoder integrated voting algorithm based on mass spectral data

作　　者：周丰丰[1] 于涛 范雨思 ZHOU Feng-feng;YU Tao;FAN Yu-si(College of Computer Science and Technology,Jilin University,Changchun 130012,China;College of Software,Jilin University,Changchun 130012,China)

机构地区：[1]吉林大学计算机科学与技术学院,长春130012 [2]吉林大学软件学院,长春130012

出　　处：《吉林大学学报（工学版）》2024年第10期2969-2977,共9页Journal of Jilin University:Engineering and Technology Edition

基　　金：国家自然科学基金项目(U19A2061);吉林省科技厅基金项目(20210509055RQ)。

摘　　要：针对质谱数据特征数量庞大,导致多疾病诊断任务复杂困难的问题,本文提出了基于质谱数据的生成对抗自编码器整合投票算法msDAGVote。使用基于双自编码器的生成对抗网络作为msDAGVote的特征提取框架,在输入质谱数据训练后,生成器子网络用于特征构造,最后通过整合投票特征选择算法对构造特征进行筛选,将获得的最优特征子集用于多疾病诊断。在10种不同疾病类型的质谱数据集上进行评估,试验数据表明:msDAGVote提取的特征优于比较方法,显著缩减分类所需特征数量的同时具备优秀的疾病分类诊断能力,在6个数据集上分类AUC超过0.98,在其余具有挑战性的数据集上超过0.87。Mass spectrometry is commonly used for disease prevention and diagnosis,but the large number of mass spectrometry data features and the wide variation of features among different diseases make the task of multi-disease diagnosis complex and difficult.To solve the above problems,this paper proposes the generative adversarial autoencoder integrated voting algorithm msDAGVote based on mass spectrometry data.The msDAGVote feature extraction framework uses a dual autoencoder-based generative adversarial network,and after the network has been trained by mass spectrometry data,the generator sub-network is used for feature construction.Evaluated using mass spectrometry datasets of 10 different disease types,the experimental data show that msDAGVote extracts better features than comparative method,significantly reduces the number of features required for classification while providing excellent diagnostic power for disease classification,with classification AUC over 0.98 on six datasets and 0.87 on the remaining challenging datasets.

关 键 词：计算机应用 生物信息学 质谱 特征工程 特征选择 对抗生成网络 双自编码器 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]