{Reference Type}: Journal Article
{Title}: Autophagy and machine learning: Unanswered questions.
{Author}: Yang Y;Pan Z;Sun J;Welch J;Klionsky DJ;
{Journal}: Biochim Biophys Acta Mol Basis Dis
{Volume}: 1870
{Issue}: 6
{Year}: 2024 08 25
{Factor}: 6.633
{DOI}: 10.1016/j.bbadis.2024.167263
{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.