Artificial Molecular Network (AMoNet): graph learning for cancer survival prediction from targeted sequencing

Abstract AMoNet (Artificial Molecular Networks) is a tool that aims to predict cancer patients’ survival when only targeted gene sequencing data are available. Outcome predictions from sparse data can benefit from new methods including deep learning. Our approach optimizes large recurrent directed molecular networks built from prior knowledge supported by speed-up computations and interpretations. Predictions suggested by the model simulations are available in a user-friendly interface.