JSM 2005 - Toronto

Abstract #304155

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.

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Legend: = Applied Session, = Theme Session, = Presenter
Activity Number: 149
Type: Luncheons
Date/Time: Monday, August 8, 2005 : 12:30 PM to 1:50 PM
Sponsor: Biopharmaceutical Section
Abstract - #304155
Title: Data-intensive Strategies in Drug Discovery: Opportunities for Statisticians
Author(s): Keith Soper*+
Companies: Merck & Co., Inc.
Address: WP37C 305, West Point, PA, 19486, United States
Keywords: drug discovery ; machine learning ; data mining ; molecular modeling
Abstract:

New strategies in drug discovery increasingly rely on complex data, making statistical contributions central to success. Examples include machine learning strategies to rescue false negatives in high throughput screens, comparison of alternative technologies in molecular profiling, telemetry to characterize individual differences in cardiac function, and chip experiments to identify subregions of antagonism or synergy. Statisticians willing to work with messy data and capable of using a wide spectrum of tools can make key contributions. I will share our approach and some success stories, and hope to learn from your experiences as well.


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Revised March 2005