Volume 30 Issue 5
Sep.  2021
Turn off MathJax
Article Contents
ZHU Rong, DAI Lingyun, LIU Jinxing, GUO Ying. Diagnostic Classification of Lung Cancer Using Deep Transfer Learning Technology and Multi-Omics Data[J]. Chinese Journal of Electronics, 2021, 30(5): 843-852. doi: 10.1049/cje.2021.06.006
Citation: ZHU Rong, DAI Lingyun, LIU Jinxing, GUO Ying. Diagnostic Classification of Lung Cancer Using Deep Transfer Learning Technology and Multi-Omics Data[J]. Chinese Journal of Electronics, 2021, 30(5): 843-852. doi: 10.1049/cje.2021.06.006

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

doi: 10.1049/cje.2021.06.006
Funds:

This work is supported in part by Shandong Social Science Planning Fund Program No. 21BTQJ02, and the National Natural Science Foundation of China under Grant No.61902215, No.61872220.

  • Received Date: 2020-09-28
    Available Online: 2021-09-02
  • 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 diagnosis. Machine 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 learning. Second, 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 model. The 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 dataset. The 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.
  • loading
  • C. M. V. Der Aalst, K. T. Haaf and H. J. De Koning, "Lung cancer screening:Latest developments and unanswered questions", The Lancet Respiratory Medicine, Vol.4, No.9, pp.749-761, 2016.
    F. E. Lennon, G. C. Cianci, N. A. Cipriani, et al., "Lung cancer-A fractal viewpoint", Nature Reviews Clinical Oncology, Vol.12, No.11, pp.664-675, 2015.
    W. D. Travis, "The 2015 WHO classification of lung tumors", Journal of Thoracic Oncology, Vol.35, No.2, pp.188-188, 2014.
    W. D. Travis, E. Brambilla, A. P. Burke, et al., "Introduction to the 2015 world health organization classification of tumors of the lung, pleura, thymus, and heart", Journal of Thoracic Oncology, Vol.10, No.9, pp.1240-1242, 2015.
    K. Inamura, "Lung cancer:Understanding its molecular pathology and the 2015 WHO classification", Frontiers in Oncology, Vol.7, Page 193, 2017.
    J. N. Weinstein, E. A. Collisson, G. B. Mills, et al., "The cancer genome atlas pan-cancer analysis project", Nature Genetics, Vol.45, No.10, pp.1113-1120, 2013.
    K. Tomczak, P. Czerwinska and M. Wiznerowicz, "The cancer genome atlas (TCGA):An immeasurable source of knowledge", Contemporary Oncology, Poznan, Poland, Vol.19, No.1, pp.68-77, 2015.
    T. K. Paul and H. Iba, "Prediction of cancer class with majority voting genetic programming classifier using gene expression data", IEEE/ACM Transactions on Computational Biology Bioinformatics, Vol.6, No.2, pp.353-367, 2009.
    M. Draminski, A. Rada-Iglesias, S. Enroth, et al., "Monte Carlo feature selection for supervised classification", Bioinformatics, Vol.24, No.1, pp.110-117, 2008.
    P. Broet, V. A. Kuznetsov, J. Bergh, et al., "Identifying gene expression changes in breast cancer that distinguish early and late relapse among uncured patients", Bioinformatics, Vol.22, No.12, pp.1477-1485, 2006.
    X. Huang, Q. Lei, T. Xie, et al., "Deep transfer convolutional neural network and extreme learning machine for lung nodule diagnosis on CT images", Knowledge-Based Systems, Vol.204, Article No.106230, 2020.
    Y. Koike, K. Aokage, K. Ikeda, et al., "Machine learning-based histological classification that predicts recurrence of peripheral lung squamous cell carcinoma", Lung cancer, Amsterdam, Netherlands, Vol.147, pp.252-258, 2020.
    H. Chen, Y. Zhang, M. K. Kalra, et al., "Low-dose CT with a residual encoder-decoder convolutional neural network", IEEE Transactions on Medical Imaging, Vol.36, No.12, pp.2524-2535, 2017.
    G. Eraslan, L. M. Simon, M. Mircea, et al., "Single-cell RNA-seq denoising using a deep count autoencoder", Nature Communications, Vol.10, Article, No.390, 2019.
    J. Xu, L. Xiang, Q. Liu, et al., "Stacked sparse autoencoder (SSAE) for nuclei detection on breast cancer histopathology images", IEEE Transactions on Medical Imaging, Vol.35, No.1, pp.119-130, 2016.
    K.-H. Yu, C. Zhang, G. J. Berry, et al., "Predicting non-small cell lung cancer prognosis by fully automated microscopic pathology image features", Nature Communications, Vol.7, Article, No.12474, 2016.
    X. Wang, A. Janowczyk, Y. Zhou, et al., "Prediction of recurrence in early stage non-small cell lung cancer using computer extracted nuclear features from digital H&E images", Scientific Reports, Vol.7, Article No.13543, 2017.
    Z. Tao, H. Bing-qiang, L. Hui-ling, et al., "Research on residual neural network and its application on medical image processing", Acta Electronica Sinica, Vol.48, No.7, pp.1436-1447, 2020.
    J. Li, J. Zhang, D. Chang, et al., "Computer-assisted detection of colonic polyps using improved faster R-CNN", Chinese Journal of Electronics, Vol.28, No.4, pp.718-724, 2019.
    P. Danaee, R. Ghaeini and D. A. Hendrix, "A deep learning approach for cancer detection and relevant gene identification", Pacific Symposium on Biocomputing, Vol.22, pp.219-229, 2017.
    A. Dhillon and G. K. Verma, "Convolutional neural network:A review of models, methodologies and applications to object detection", Progress in Artificial Intelligence, Vol.9, No.2, pp.85-112, 2020.
    F. Rosenblatt, "The perceptron:A probabilistic model for information storage and organization in the brain", Psychological Review, Vol.65, No.6, Page 386, 1958.
    F. Fan, W. Cong and G. Wang, "Generalized backpropagation algorithm for training second-order neural networks", International Journal for Numerical Methods in Biomedical Engineering, Vol.34, No.5, DOI:10.1002/cnm.2956, 2018.
    A. R. Heravi and G. A. Hodtani, "A new correntropy-based conjugate gradient backpropagation algorithm for improving training in neural networks", IEEE Transactions on Neural Networks and Learning Systems, Vol.29, No.12, pp.6252-6263, 2018.
    H. Kamyshanska and R. Memisevic, "The potential energy of an autoencoder", IEEE Transactions on Pattern Analysis Machine Intelligence, Vol.37, No.6, pp.1261-1273, 2015.
    Y. Bengio, A. Courville and P. Vincent, "Representation learning:A review and new perspectives", IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol.35, No.8, pp.1798-1828, 2013.
    B. Ma, F. Meng, G. Yan et al., "Diagnostic classification of cancers using extreme gradient boosting algorithm and multiomics data", Computers in Biology and Medicine, Vol.121, Article No.103761, 2020.
    B. Berger, J. Peng and M. Singh, "Computational solutions for omics data", Nature Reviews Genetics, Vol.14, No.5, pp.333-346, 2013.
  • 加载中

Catalog

    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索

    Article Metrics

    Article views (180) PDF downloads(31) Cited by()
    Proportional views
    Related

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return