Deep learning-based multiomics integration model for predicting axillary lymph node metastasis in breast cancer.

Journal: Future oncology (London, England)
PMID: 37489287

Abstract

To develop a deep learning-based multiomics integration model. Five types of omics data (mRNA, DNA methylation, miRNA, copy number variation and protein expression) were used to build a deep learning-based multiomics integration model a deep neural network, incorporating an attention mechanism that adaptively considers the weights of multiomics features. Compared with other methods, the deep learning-based multiomics integration model achieved remarkable results, with an area under the curve of 0.89 (95% CI: 0.863-0.910). The deep learning-based multiomics integration model achieved promising results and is an effective method for predicting axillary lymph node metastasis in breast cancer.

Authors

  • Xue Li
    Department of Clinical Research Center, Dazhou Central Hospital, Dazhou 635000, China.
  • Lifeng Yang
    College of Information and Computer, Taiyuan University of Technology, Jinzhong, China.
  • Xiong Jiao
    College of Biomedical Engineering, Taiyuan University of Technology, Jinzhong, Shanxi, China. Electronic address: jiaoxiong@tyut.edu.cn.

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