Juergen Hahn, Ph.D., ARI Scientific Advisory Board member, discusses how using machine learning can lead to biomarker discoveries in autism research. He reviews our current understanding of autism as a balance between genetics and environment. The speaker describes the folate-dependent once-carbon metabolism and transsulfuration process and details analysis of collected data to assess parameters and important variables. Hahn describes accuracy of autism risk predictions based on the analyses described, concluding that metabolites from a mother during pregnancy cannot be used to predict development of autism. The presenter emphasizes taking a systems approach to autism research and understanding before the Q&A.