Rapid advances in biological research over recent years have significantly enriched
biological and medical data resources. Deep learning-based techniques have been successfully
utilized to process data in this field, and they have exhibited state-of-the-art performances even on
high-dimensional, nonstructural, and black-box biological data. The aim of the current study is to
provide an overview of the deep learning-based techniques used in biology and medicine and their
state-of-the-art applications. In particular, we introduce the fundamentals of deep learning and then
review the success of applying such methods to bioinformatics, biomedical imaging, biomedicine,
and drug discovery. We also discuss the challenges and limitations of this field, and outline possible
directions for further research.
High-dimensional flow law parameter calibration and uncertainty quantification over Antarctic ice shelves: a variational Bayesian approach using deep learning
