MicroRNAs play a central role in controlling gene expression, yet how cells select the active strand from each microRNA precursor has remained unclear. In this study, ASU researchers demonstrate that strand selection follows conserved structural and sequence-based rules rather than chance. Using a newly developed high-throughput assay and a machine-learning model trained on thousands of experimental measurements, the team shows that these rules apply across tissues, development, and evolution—from nematodes to humans. By establishing the first unified framework for predicting microRNA strand usage, the work reveals an unrecognized layer of gene regulation and underscores the power of artificial intelligence to uncover fundamental biological principles.