A semi-supervised deep learning method based on stacked sparse auto-encoder for cancer prediction using RNA-seq data. (November 2018)

• Record Type: Journal Article
• Authors: Xiao, Yawen; Wu, Jun; Lin, Zongli; Zhao, Xiaodong
• Is Part Of: Computer methods and programs in biomedicine. Volume 166(2018)
• Page Start: 99
• Page End: 105
• Publication Date: 2018-11
• Subjects: Stacked sparse auto-encoder -- Cancer prediction -- Gene expression data -- Semi-supervised learning -- Deep learning; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2018.10.004
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: A deep learning model, the stacked sparse auto-encoder based model, is proposed for cancer prediction. The deep learning model, with pre-training and sparsity, outperforms classical models. Important and abstract features are extracted. Prediction results are presented on lung, stomach and breast cancer data. Abstract: Background and objective: Cancer has become a complex health problem due to its high mortality. Over the past few decades, with the rapid development of the high-throughput sequencing technology and the application of various machine learning methods, remarkable progress in cancer research has been made based on gene expression data. At the same time, a growing amount of high-dimensional data has been generated, such as RNA-seq data, which calls for superior machine learning methods able to deal with mass data effectively in order to make accurate treatment decision. Methods: In this paper, we present a semi-supervised deep learning strategy, the stacked sparse auto-encoder (SSAE) based classification, for cancer prediction using RNA-seq data. The proposed SSAE based method employs the greedy layer-wise pre-training and a sparsity penalty term to help capture and extract important information from the high-dimensional data and then classify the samples. Results: We tested the proposed SSAE model on three public RNA-seq data sets of three types of cancers and compared the prediction performance with several commonly-used classification methods. The results indicate that our approach outperforms the other methods for all the three cancer data sets in various metrics. Conclusions: The proposed SSAE based semi-supervised deep learning model shows its promising ability to process high-dimensional gene expression data and is proved to be effective and accurate for cancer prediction.