Moore's research focuses on the development and application of artificial intelligence and machine learning methods for modeling complex patterns in biomedical big data.

A central focus is using informatics methods for identifying combinations of DNA sequence variations and environmental factors that are predictive of human health and complex disease.

He then applied MDR for an improved understanding of the interplay of multiple genetic polymorphisms of complex traits in genome-wide association studies.

More recent work focuses on computational methods such as the tree-based pipeline optimization tool (TPOT)[3][4] for automated machine learning and data science.

His translational bioinformatics research program has been continuously funded by multiple grants from the National Institutes of Health for nearly 20 years.