DNNs for genomics

The field of genomics has benefitted primarily due to the technological advancements in NGS, which can generate genomics data at a low cost and scale. This opened opportunities for many areas of science, such as bioinformatics and ML. Bioinformatics, the field of computational algorithms used to process biological data, was hugely successful in genomics, enabling rules to mine insights from the data. ML methodologies have a huge influence on genomics for solving some complex biological problems such as gene signatures, functional genomics, gene interactions, and so on. However, the current advances in genomics technologies, coupled with ever-growing genomics data, have created lots of new challenges that require highly accurate and sophisticated algorithms.

DL, a component of ML, has made significant strides in the areas of speech recognition, computer vision, machine translation, NLP, and so on. Because of DL’s ability to solve complex tasks with high accuracy...