Before revision: The understanding of variations in genome sequences assists us in identifying people who are predisposed to common diseases , solving rare diseases, and finding the corresponding population group of the individuals from a larger population group. Although classical machine learning techniques allow researchers to identify groups (i. e. clusters) of related variables, the accuracy, and effectiveness of these methods diminish for large and high-dimensional datasets such as the whole human genome. On the other hand , deep neural network architectures (the core of deep learning) can better exploit large-scale datasets to build complex models .
After revision: The understanding of variations in genome sequences assists us in identifying people who are predisposed to common diseases and explain differences in disease susceptibility and how patients react to drugs. Machine learning algorithms are increasingly being applied to identify interacting GVs to understand their complex phenotypic traits. Since the performance of a learning algorithm not only depends on the size and nature of the data but also on the quality of underlying representation , deep neural network architectures (the core of deep learning) can better exploit large-scale datasets to build complex models .