A benchmark study of deep learning-based multi-omics data fusion methods for cancer.

Journal: Genome biology
Published Date: Aug 9, 2022

Abstract

BACKGROUND: A fused method using a combination of multi-omics data enables a comprehensive study of complex biological processes and highlights the interrelationship of relevant biomolecules and their functions. Driven by high-throughput sequencing technologies, several promising deep learning methods have been proposed for fusing multi-omics data generated from a large number of samples.

Authors

  • Dongjin Leng
    Beijing Institute of Radiation Medicine, Beijing, China.
  • Linyi Zheng
    School of Informatics, Xiamen University, Xiamen, People's Republic of China.
  • Yuqi Wen
    Beijing Institute of Radiation Medicine, Beijing, China.
  • Yunhao Zhang
    State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, CAS, Beijing, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China.
  • Lianlian Wu
    Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China.
  • Jing Wang
    Endoscopy Center, Peking University Cancer Hospital and Institute, Beijing, China.
  • Meihong Wang
  • Zhongnan Zhang
  • Song He
    Department of Biotechnology, Beijing Institute of Radiation Medicine, 27 Taiping Street, Haidian District, Beijing, 100850, China.
  • Xiaochen Bo
    Beijing Institute of Radiation Medicine, 27 Taiping Road, Beijing 100850, China.

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