The explosion of molecular and cellular profile data from vast numbers of samples has resulted from technological developments in genomics and imaging. Traditional analytic methodologies are being tested by the fast rise in biological data dimension and collection rate. Modern machine learning approaches, such as deep learning, promise to make accurate predictions by using very large data sets to identify hidden structure. We explore regulatory genomics and cellular imaging applications of this new breed of analytic techniques in this study. We give an overview of what deep learning is and how it may be used to obtain biological insights in various scenarios. We emphasise potential dangers and restrictions to educate computational biologists when and how to make the most of this new tool, in addition to showing particular applications and offering recommendations for practical use.
