Abstract #301790

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JSM 2003 Abstract #301790
Activity Number: 219
Type: Contributed
Date/Time: Tuesday, August 5, 2003 : 9:00 AM to 10:50 AM
Sponsor: Biopharmaceutical Section
Abstract - #301790
Title: Mining Nuggets of Activity in High-dimensional Space from High-throughput Screening Data
Author(s): Yuanyuan Wang*+ and Hugh A. Chipman
Companies: University of Waterloo and University of Waterloo
Address: Dept. of Statistics & Acturial Science, Waterloo, ON, N2L 3G1, Canada
Keywords: high-throughput screening ; KNN ; model-averaging ; data mining ; recursive partitioning
Abstract:

High-throughput screening (HTS) is often used in drug discovery to screen large numbers of compounds against a biological target. Statistical analysis of HTS data is aimed at uncovering the relationship between chemical structure, as quantified by various numerical descriptor variables, and biological activity. Estimation of this structure-activity relationship enables prediction of biologically active molecules in huge collections of potential drug compounds. There is much previous work showing that local methods, like K-nearest neighbors (KNN) and classification and regression trees (CART), perform very well. Some adaptations of KNN and CART, including varying K in KNN, averaging over subsets of explanatory variables, bagging, and boosting, have also been considered. (Based on joint work with Hugh Chipman and William Welch at the University of Waterloo.)

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