JSM 2004 - Toronto

Abstract #300062

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Activity Number: 96
Type: Invited
Date/Time: Monday, August 9, 2004 : 10:30 AM to 12:20 PM
Sponsor: SSC
Abstract - #300062
Title: Averaging Methods for High-throughput Screening Data
Author(s): Yuanyuan (Marcia) Wang*+
Companies: University of Waterloo
Address: 200 Elm St., Toronto, ON, M5T 1K4, Canada
Keywords: HTS ; averaging methods ; KNN ; CART ; bias and variance trade-off
Abstract:

In drug discovery, high-throughput screening (HTS) is used to assay large numbers of compounds against a biological target. A research pharmaceutical company might have of the order 2 million compounds available, and the human genome project is generating many new targets. Hence, there is a need for a more efficient strategy: smart or virtual screening. In smart screening, a representative sample (experimental design) of compounds is selected from a collection and assayed against a target. A model is built relating activity to explanatory variables describing compound structure. Our previous work shows that local methods, like K-nearest neighbors (KNN) and classification and regression trees (CART), perform very well. I will propose a method of averaging across multiple classifiers based on building classifiers on subspaces (subsets of variables). It improves the performance of KNN and CART for HTS data. Some interpretation of the method has also been considered.

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