Deep Learning and Its Applications in Biomedicine

Genomics Proteomics Bioinformatics. 2018 Feb;16(1):17-32. doi: 10.1016/j.gpb.2017.07.003. Epub 2018 Mar 6.


Advances in biological and medical technologies have been providing us explosive volumes of biological and physiological data, such as medical images, electroencephalography, genomic and protein sequences. Learning from these data facilitates the understanding of human health and disease. Developed from artificial neural networks, deep learning-based algorithms show great promise in extracting features and learning patterns from complex data. The aim of this paper is to provide an overview of deep learning techniques and some of the state-of-the-art applications in the biomedical field. We first introduce the development of artificial neural network and deep learning. We then describe two main components of deep learning, i.e., deep learning architectures and model optimization. Subsequently, some examples are demonstrated for deep learning applications, including medical image classification, genomic sequence analysis, as well as protein structure classification and prediction. Finally, we offer our perspectives for the future directions in the field of deep learning.

Keywords: Big data; Bioinformatics; Biomedical informatics; Deep learning; High-throughput sequencing; Medical image.

Publication types

  • Research Support, Non-U.S. Gov't
  • Review

MeSH terms

  • Algorithms*
  • Computational Biology / methods*
  • Diagnostic Imaging*
  • Genomics / methods*
  • Humans
  • Image Interpretation, Computer-Assisted / methods
  • Machine Learning*
  • Neural Networks, Computer*
  • Protein Structure, Secondary
  • Proteins / metabolism*


  • Proteins