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Title
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* ! Data Mining Techniques in Drug Discovery and Development
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Date / Time / Room
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Sponsor
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Type
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08/14/2002
10:30 AM -
12:20 PM
Room: H-East Ballroom
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Biopharmaceutical Section*, ENAR, SPAIG Committee, Section on Health Policy Statistics*, WNAR
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Invited
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Organizer:
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Amarjot Kaur, Merck & Company, Inc.
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Chair:
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Bret Musser, Merck & Company, Inc.
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Discussant:
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11:50 AM - Robert Stine, University of Pennsylvania, Wharton School
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Floor Discussion
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12:10 PM
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Description
The drug discovery and development databases have grown tremendously and continue to grow with recent advances in technology. In the process of drug discovery, several automated techniques are employed to capture vast bodies of data - some typical examples include high throughput screening and microarray gene expression experiments. Further, at the stage of drug development, clinical trials entail recording numerous measurements, at multiple time points, on several patients. In a typical clinical trial, vast amount of ancillary patient data is also captured in addition to the specific information collected to address the objectives of the study. The existence of such large bodies of data naturally prompts one to suspect that there may be interesting and undiscovered nuggets of valuable information concealed within them. The goal of this session is to bring forth the challenges associated with exploration of large databases in detecting interesting patterns or structures. In this session, we will explore the recent statistical developments, techniques, and issues related to large pharmaceutical data, collected at the time of drug discovery and development.
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