(Team 2) Data to Knowledge in Pharmaceutical Research

Monday, August 9, 2004 - 10:00am - 10:20am
Keller 3-180
Ann Dewitt (3M)
This project addresses fundamental, computational needs in pharmaceutical research, that is, understanding how and what raw data is generated, finding best methods to clean data, and then finally using this analyzed data with other results from different experiments to test hypotheses and discern relationships. Some proficiency in dealing with many rows of data (1000's to 10,000's) will be helpful.

Measurements collected from living organisms often have a high degree of variability, particularly when probed in a higher throughput fashion. Given one set of bench-scale biological data with a variety of controls and references, determine a method to best identify “hits” given expert opinion. Given the same basic biologic data, except generated in high-throughput fashion, determine a method identify “hits.” Compare the bench-scale to the high-throughput results. Finally, examine possible relationships between these results and additional given chemical and biological results.


Improved Statistical Methods for Hit Selection in High-Throughput Screening. Brideau C. et al. Journal of Biomolecular Screening 8(6); pp.634-647.

Visual and computational analysis of structure-activity relationships in high-throughput screening data. P. Gedeck. Current opinion in Chemical Biology. V 5; pp 389-395.

Mining nuggets of activity in high dimensional space from high throughput screening data.

The Immune Response Modifier Resiquimod Mimics CD40-Induced B Cell Activation. Bishop G. et al. Cellular Immunology V 208; pp. 9-17.

Building with a scaffold: emerging strategies for high to low level cellular modeling. T Ideker. Trends in Biotechnology, V 21, Iss 6, pp. 255-262.