As the volume and velocity of medical publications have increased at an unprecedented pace, a computational-based learning system is essential to avoid expensive and time-consuming human annotations which in general hinders the deployment of novel therapeutic methods in clinical practice. In this talk, I will present Medreader, a novel multi-channel learning system that can summarize (topic learning), understand (knowledge-graph constructing) and generalize (hypothesis generating) knowledge simultaneously from query-related publications. Medreader can access how faithfully a discovered concept is by using data beyond publications and conducting an novel enrichment analysis.