复制
计算机科学
错误发现率
鉴定(生物学)
预处理器
数据挖掘
吞吐量
药物发现
多重比较问题
机器学习
人工智能
生物信息学
统计
生物
数学
电信
基因
植物
生物化学
无线
作者
Nathalie Malo,James A. Hanley,Sonia Cerquozzi,Jerry Pelletier,Robert Nadon
摘要
High-throughput screening is an early critical step in drug discovery. Its aim is to screen a large number of diverse chemical compounds to identify candidate 'hits' rapidly and accurately. Few statistical tools are currently available, however, to detect quality hits with a high degree of confidence. We examine statistical aspects of data preprocessing and hit identification for primary screens. We focus on concerns related to positional effects of wells within plates, choice of hit threshold and the importance of minimizing false-positive and false-negative rates. We argue that replicate measurements are needed to verify assumptions of current methods and to suggest data analysis strategies when assumptions are not met. The integration of replicates with robust statistical methods in primary screens will facilitate the discovery of reliable hits, ultimately improving the sensitivity and specificity of the screening process.
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