ABSTRACT
Genomics is about understanding the fundamentals of the genome. Its applications range from medicine and pharmacy to agriculture, and beyond. Recent advancements in genomic technology, such as high-throughput sequencing technologies, have allowed modern genomics to step into the “big-data” era. However, conventional analytic methods are no longer sufficient when facing these high dimensional data sets. Conversely, deep learning (DL) methods are capable of identifying highly complex patterns and have achieved superhuman performance in many tasks related to genomics. DL entails a series of robust algorithms that have succeeded in a variety of fields such as computer vision, speech generation, and natural language processing. In this chapter, we provide a brief history and a theoretical introduction to genomics, before delving into practical guidance on effectively applying DL. Finally, we survey state-of-the-art applications of DL in several aspects of genomic research.
