4.6 Article

Deep learning approach for cancer subtype classification using high-dimensional gene expression data

期刊

BMC BIOINFORMATICS
卷 23, 期 1, 页码 -

出版社

BMC
DOI: 10.1186/s12859-022-04980-9

关键词

Cancer subtype; Classification; Deep learning

资金

  1. National Natural Science Foundation of China [61972134]
  2. Young Elite Teachers in Henan Province [2020GGJS050]
  3. Doctor Foundation of Henan Polytechnic University [B2018-36]
  4. Innovative and Scientific Research Team of Henan Polytechnic University [T2021-3]

向作者/读者索取更多资源

This paper proposes a deep learning approach, DCGN, which combines a CNN and BiGRU to achieve nonlinear dimensionality reduction and feature learning for eliminating irrelevant factors in gene expression data. Experimental results demonstrate that DCGN outperforms seven other cancer subtype classification methods.
Motivation Studies have shown that classifying cancer subtypes can provide valuable information for a range of cancer research, from aetiology and tumour biology to prognosis and personalized treatment. Current methods usually adopt gene expression data to perform cancer subtype classification. However, cancer samples are scarce, and the high-dimensional features of their gene expression data are too sparse to allow most methods to achieve desirable classification results. Results In this paper, we propose a deep learning approach by combining a convolutional neural network (CNN) and bidirectional gated recurrent unit (BiGRU): our approach, DCGN, aims to achieve nonlinear dimensionality reduction and learn features to eliminate irrelevant factors in gene expression data. Specifically, DCGN first uses the synthetic minority oversampling technique algorithm to equalize data. The CNN can handle high-dimensional data without stress and extract important local features, and the BiGRU can analyse deep features and retain their important information; the DCGN captures key features by combining both neural networks to overcome the challenges of small sample sizes and sparse, high-dimensional features. In the experiments, we compared the DCGN to seven other cancer subtype classification methods using breast and bladder cancer gene expression datasets. The experimental results show that the DCGN performs better than the other seven methods and can provide more satisfactory classification results.

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