Interpretable deep learning methods for multiview learning.

Journal: BMC bioinformatics
PMID: 38350879

Abstract

BACKGROUND: Technological advances have enabled the generation of unique and complementary types of data or views (e.g. genomics, proteomics, metabolomics) and opened up a new era in multiview learning research with the potential to lead to new biomedical discoveries.

Authors

  • Hengkang Wang
    Department of Computer Science and Engineering, University of Minnesota, Minneapolis, 55455, USA.
  • Han Lu
    Division of Biostatistics and Health Data Science, University of Minnesota, Minneapolis, 55414, USA.
  • Ju Sun
    Department of Computer Science and Engineering, University of Minnesota, Minneapolis, Minnesota, USA.
  • Sandra E Safo
    Division of Biostatistics and Health Data Science, University of Minnesota, Minneapolis, 55414, USA. ssafo@umn.edu.

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