Publication

Abstract : High-throughput technologies in genetics are now generating enormous datasets, which shed light on bewildering relationships between genes and human health. However, a huge challenge remains in interpreting the data to generate actionable knowledge. This is where explainable artificial intelligence (XAI) could be powerful enough to bridge this gap and bring that understanding to humans regarding complex discoveries in genetics. This chapter introduces the potential of XAI in unlocking the meaning within genetic data on the basis of a better understanding of risk prediction of disease, drug discovery, and personalized medicine. We describe the use of different models based on various genetic data and present the upsides and downsides of these models. This chapter also discusses some of the ethical and possible biases of using XAI in such research. We emphasize the collaborative potential of combining human expertise with XAI for the realization of these genetic discoveries' full potential toward the advance of human health.