Visible machine learning for predictive oncology and other genotype-phenotype challenges. We are developing “visible” machine learning approaches to model the flow of genetic information, in which predictive models learn not only to translate genotype to phenotype but to also identify the molecular functions and mechanisms by which these predictions are made. The central concept is to couple the structure of a machine learning model to the structure and function of a target biological system, creating what we have called visible neural networks (VNNs). Whereas a typical predictive model is not readily interpretable due to many hidden variables and states (a “black box”), VNNs can be more directly inspected to reveal the molecular and cellular events responsible for each prediction. They provide a framework for interpretable deep learning, combining the generality, scale and power of neural networks with a biomechanistic understanding.