Diagnostic Classification of Lung Cancer Using Deep Transfer Learning Technology and Multi-Omics Data

作     者：ZHU Rong DAI Lingyun LIU Jinxing GUO Ying ZHU Rong;DAI Lingyun;LIU Jinxing;GUO Ying

作者机构：School of Computer Science Qufu Normal University School of Automation Central South University 

出 版 物：《Chinese Journal of Electronics》 (电子学报(英文))

年 卷 期：2021年第30卷第5期

页      面：843-852页

核心收录：

学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 1002[医学-临床医学] 081104[工学-模式识别与智能系统] 08[工学] 100214[医学-肿瘤学] 0835[工学-软件工程] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 10[医学] 

基　　金：supported in part by Shandong Social Science Planning Fund Program No. 21BTQJ02 the National Natural Science Foundation of China under Grant No.61902215, No.61872220 

主　　题：machine learning models early stages genetics lung image classification convolutional autoencoder approach patient diagnosis genomics cancer cancer classification labeled target datasets late stages gene expression level image coding multiomics data deep learning (artificial intelligence) diagnostic classification early diagnosis omics data lung cancer diagnosis convolutional neural network method bioinformatics combined convolutional neural network model migration rules migration learning integrated dataset deep migratory learning classification model machine learning algorithms lung cancer gene datasets high-throughput sequencing technology deep transfer learning technology diagnostic radiography 

摘      要：In recent years, with the increasing application of highthroughput sequencing technology,researchers have obtained and accumulated a large amount of multi-omics data, making it possible to diagnose cancer at the gene expression level. The proliferation of various omics data can provide a large amount of biological information, which brings new opportunities and great challenges as well to cancer classification and *** learning algorithms for early diagnosis of lung cancer have emerged that distinguish cancers of the early and late stages by using genomic features. Omics data are generally characterized with low sample size,high dimensionality and high noise. Therefore, simple direct application of common classification methods cannot achieve better performance and must be improved in a targeted manner. This paper puts forward a combined convolutional neural network and convolutional autoencoders approach to construct a deep migratory learning classification model for early lung cancer diagnosis. First,the convolutional auto-encoders algorithm is used to reduce the dimensionality of the dataset in order to make it better meet the requirements of migration ***, a neural network model is constructed with the original dataset and the existing labeled dataset, and the model migration rules are set as well. Finally, a small number of labeled target datasets are used in the training to complete the construction of the classification *** proposed convolutional neural network method based on model migration and five other popular machine learning models are used to classify and predict the three lung cancer gene datasets and the integrated *** experimental results show that such four evaluation metrics as accuracy, precision, recall, and f1-score with our proposed method have obtained better prediction performance, and the average area under curve result also shows our proposed method is optimal.