J Biomed Transl Res.  2019 Mar;20(1):15-20. 10.12729/jbtr.2019.20.1.015.

Classification of stomach cancer gene expression data using CNN algorithm of deep learning

  • 1Medical Research Institute, Chungbuk National University, Cheongju 28644, Korea.
  • 2College of Electrical and Computer Engineering, Chungbuk National University, Cheongju 28644, Korea.
  • 3Department of Nursing, Woosong College, Deajeon 34518, Korea.
  • 4Department of Biomedical Engineering, School of Medicine, Chungbuk National University, Cheongju 28644, Korea. kimka@chungbuk.ac.kr


The incidence of stomach cancer has been found to be gradually decreasing; however, it remains one of the most frequently occurring malignant cancers in Korea. According to statistics of 2017, stomach cancer is the top cancer in men and the fourth most important cancer in women, necessitating methods for its early detection and treatment. Considerable research in the field of bioinformatics has been conducted in cancer studies, and bioinformatics approaches might help develop methods and models for its early prediction. We aimed to develop a classification method based on deep learning and demonstrate its application to gene expression data obtained from patients with stomach cancer. Data of 60,483 genes from 334 patients with stomach cancer in The Cancer Genome Atlas were evaluated by principal component analysis, heatmaps, and the convolutional neural network (CNN) algorithm. We combined the RNA-seq gene expression data with clinical data, searched candidate genes, and analyzed them using the CNN deep learning algorithm. We performed learning using the sample type and vital status of patients with stomach cancer and verified the results. We obtained an accuracy of 95.96% for sample type and 50.51% for vital status. Despite overfitting owing to the limited number of patients, relatively accurate results for sample type were obtained. This approach can be used to predict the prognosis of stomach cancer, which has many types and underlying causes.


gene expression data; deep learning; convolutional neural network; principal component analysis; heatmap
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