Autophagy and machine learning: Unanswered questions.

Journal: Biochimica et biophysica acta. Molecular basis of disease
PMID:

Abstract

Autophagy is a critical conserved cellular process in maintaining cellular homeostasis by clearing and recycling damaged organelles and intracellular components in lysosomes and vacuoles. Autophagy plays a vital role in cell survival, bioenergetic homeostasis, organism development, and cell death regulation. Malfunctions in autophagy are associated with various human diseases and health disorders, such as cancers and neurodegenerative diseases. Significant effort has been devoted to autophagy-related research in the context of genes, proteins, diagnosis, etc. In recent years, there has been a surge of studies utilizing state of the art machine learning (ML) tools to analyze and understand the roles of autophagy in various biological processes. We taxonomize ML techniques that are applicable in an autophagy context, comprehensively review existing efforts being taken in this direction, and outline principles to consider in a biomedical context. In recognition of recent groundbreaking advances in the deep-learning community, we discuss new opportunities in interdisciplinary collaborations and seek to engage autophagy and computer science researchers to promote autophagy research with joint efforts.

Authors

  • Ying Yang
    Department of Endocrinology, The Affiliated Hospital of Yunnan University, Kunming, China.
  • Zhaoying Pan
    Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, MI 48109, USA.
  • Jianhui Sun
    Department of Computer Science, University of Virginia, Charlottesville, VA 22903, USA.
  • Joshua Welch
    Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, MI 48109, USA; Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA.
  • Daniel J Klionsky
    Department of Molecular, Cellular, and Developmental Biology, University of Michigan, Ann Arbor, MI 48109, USA; Life Sciences Institute, University of Michigan, Ann Arbor, MI 48109, USA. Electronic address: klionsky@umich.edu.