Altekar Maneesha, Homon Carol A, Kashem Mohammed A, Mason Steven W, Nelson Richard M, Patnaude Lori A, Yingling Jeffrey, Taylor Paul B
Hercules, 1313 North Market Street, Wilmington, DE 19894-0001, USA.
Clin Lab Med. 2007 Mar;27(1):139-54. doi: 10.1016/j.cll.2007.01.001.
With the transition from manual to robotic HTS in the last several years, assay optimization has become a significant bottleneck. Recent advances in robotic liquid handling have made it feasible to reduce assay optimization timelines with the application of statistically designed experiments. When implemented, they can efficiently optimize assays by rapidly identifying significant factors, complex interactions, and nonlinear responses. This article focuses on the use of statistically designed experiments in assay optimization.
在过去几年中，随着从手动到机器人高通量筛选（HTS）的转变，实验优化已成为一个重大瓶颈。机器人液体处理技术的最新进展使得通过应用统计设计实验来缩短实验优化时间线成为可能。当实施这些实验时，它们可以通过快速识别显著因素、复杂相互作用和非线性响应来有效地优化实验。本文重点介绍统计设计实验在实验优化中的应用。
