Lab-in-the-Loop

Overview

Lab-in-the-loop describes the iterative cycle of algorithm prediction → wet-lab experiment → training data contribution → improved algorithm. It applies active learning to biology, a domain where data is expensive and slow to generate relative to computational domains. Rather than training once on a fixed dataset, the approach continuously improves models by targeting experiments at the most informative regions of biological space.