An overview of artificial intelligence in the field of genomics

Abstract

Abstract Artificial intelligence (AI) is revolutionizing many real-world applications in various domains. In the field of genomics, multiple traditional machine-learning approaches have been used to understand the dynamics of genetic data. These approaches provided acceptable predictions; however, these approaches are based on opaque-box AI algorithms which are not able to provide the needed transparency to the community. Recently, the field of explainable artificial intelligence has emerged to overcome the interpretation problem of opaque box models by aiming to provide complete transparency of the model and its prediction to the users especially in sensitive areas such as healthcare, finance, or security. This paper highlights the need for eXplainable Artificial Intelligence (XAI) in the field of genomics and how the understanding of genomic regions, specifically the non-coding regulatory region of genomes (i.e., enhancers), can help uncover underlying molecular principles of disease states, in particular cancer in humans.