Multi-omics integration method based on attention deep learning network for biomedical data classification.

Journal: Computer methods and programs in biomedicine
Published Date: Jan 27, 2023

Abstract

BACKGROUND AND OBJECTIVE: Integrating multi-omics data for the comprehensive analysis of the biological processes in human diseases has become one of the most challenging tasks of bioinformatics. Deep learning (DL) algorithms have recently become one of the most promising multi-omics data integration analysis methods. However, existing DL-based studies almost integrate the multi-omics data by concatenation in the input data space or the learned feature space, ignoring the correlations between patients and omics.

Authors

  • Ping Gong
    Deepwise AI Lab, Beijing Deepwise & League of PHD Technology Co., Ltd., Beijing, China.
  • Lei Cheng
    State Key Laboratory of Oral Diseases, Sichuan University, Chengdu, China.
  • Zhiyuan Zhang
    Sichuan Academy of Agricultural Science, Institute of Agricultural Resources and Environment, SAAS, Institute of Edible Fungi, Shizishan Road NO. 4, Jinjiang District, Chengdu, 610066, China.
  • Ao Meng
    School of Medical Imaging, Xuzhou Medical University, Xuzhou, CN, China.
  • Enshuo Li
    School of Medical Imaging, Xuzhou Medical University, Xuzhou, CN, China.
  • Jie Chen
    School of Basic Medical Sciences, Health Science Center, Ningbo University, Ningbo, China.
  • Longzhen Zhang
    Department of Radiation Oncology, Affiliated Hospital of Xuzhou Medical University, Xuzhou, CN, China.

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