JSM 2005 Online Program

Abstract #304762

This is the preliminary program for the 2005 Joint Statistical Meetings in Minneapolis, Minnesota. Currently included in this program is the "technical" program, schedule of invited, topic contributed, regular contributed and poster sessions; Continuing Education courses (August 7-10, 2005); and Committee and Business Meetings. This on-line program will be updated frequently to reflect the most current revisions.

To View the Program:
You may choose to view all activities of the program or just parts of it at any one time. All activities are arranged by date and time.

The views expressed here are those of the individual authors
and not necessarily those of the ASA or its board, officers, or staff.

The Program has labeled the meeting rooms with "letters" preceding the name of the room, designating in which facility the room is located:

Minneapolis Convention Center = “MCC” Hilton Minneapolis Hotel = “H” Hyatt Regency Minneapolis = “HY”

Back to main JSM 2005 Program page

Legend:

= Applied Session,

= Theme Session,

= Presenter

Activity Number:	522
Type:	Contributed
Date/Time:	Thursday, August 11, 2005 : 10:30 AM to 12:20 PM
Sponsor:	Biopharmaceutical Section
Abstract - #304762
Title:	Statistical Design and Analysis of Pooling Experiments
Author(s):	Katja Remlinger*+ and Jacqueline M. Hughes-Oliver
Companies:	GlaxoSmithKline and North Carolina State University
Address:	5412 Silver Moon Lane, Raleigh, NC, 27606, United States
Keywords:	Drug discovery ; Minimal collision ; Molecular descriptors ; Multiple trees ; Tanimoto similarity ; Uniform cell coverage designs
Abstract:	Discovery of a new drug involves screening large chemical libraries to identify new and diverse active compounds. Screening efficiency can be improved by testing compounds in pools. We consider two criteria to design pools: optimal coverage of the chemical space and minimal collision between compounds within a pool. Five pooling designs are applied to a public dataset. We evaluate each method by determining how well the design criteria are met. One pooling design, MC, emerges as a winner, but all designed pools clearly outperform randomly created pools. Furthermore, different analysis approaches of the pooling designs are investigated. Multiple trees are compared to model-based likelihood approaches with different covariate class definitions. Results show a model-based likelihood approach with a multiple-trees-lower-bound covariate class definition gives the best performance.

The address information is for the authors that have a + after their name.
Authors who are presenting talks have a * after their name.

Back to the full JSM 2005 program