Statistical considerations for high throughput screening data

作     者：Xian-Jin XIE 

作者机构：Division of BiostatisticsDepartment of Clinical Sciences&Simmons Comprehensive Cancer CenterThe University of Texas Southwestern Medical CenterDallasTexas 75390USA 

出 版 物：《Frontiers in Biology》 (生物学前沿（英文版）)

年 卷 期：2010年第5卷第4期

页      面：354-360页

核心收录：

学科分类：0809[工学-电子科学与技术（可授工学、理学学位）] 08[工学] 

基　　金：This work is supported in part by NIH P50-CA70907 NIH U24CA126608 and NASA NNJ05HD36G 

主　　题：high throughput screen false-positive rate false-negative rate target discovery predictive modeling 

摘      要：High throughput screening(HTS)is a widely used effective approach in genome-wide association and large scale protein expression studies,drug discovery,and biomedical imaging *** to accurately identify candidate‘targets’or biologically meaningful features with a high degree of confidence has led to extensive statistical research in an effort to minimize both false-positive and false-negative rates.A large body of literature on this topic with in-depth statistical contents is *** examine currently available statistical methods on HTS and aim to summarize some selected methods into a concise,easy-tofollow introduction for experimental biologists.