Jeffrey D. Yingling

Papers

Total Citations

H-Index

About

Dr. Jeffrey D. Yingling is a leading expert in the application of Design of Experiments (DoE) to high-throughput screening (HTS) and assay development. His most influential work, "Assay Optimization: A Statistical Design of Experiments Approach" (2007), has garnered 46 citations, establishing him as a key figure in bridging the gap between statistical methodology and practical laboratory workflows. Dr. Yingling’s major contribution lies in demonstrating how robotic liquid handling advances have made DoE a feasible and powerful tool for dramatically reducing assay optimization timelines, a critical bottleneck in drug discovery. His research provides a systematic framework for moving beyond traditional one-factor-at-a-time methods, enabling researchers to achieve robust, reproducible assays with fewer experiments. By translating complex statistical concepts into actionable protocols, Dr. Yingling has empowered scientists to enhance data quality and accelerate the transition from manual to automated HTS systems. His work remains a foundational reference for anyone seeking to improve efficiency and reliability in early-stage pharmaceutical development.