Least-Squares Curve-Fitting Techniques For Improved Medicine Development
Rensselaer Polytechnic Institute
January 10, 2012
Bailey Hall 207
Refreshments will be served in Bailey Hall 204 at 4:45
In drug discovery, the effectiveness of medicines is based on fitting the Hill equation (shaped like a sigmoid) to experimental data points. These data points are sometimes noisy, and often fail to cover the full range of the sigmoid. This makes curve fitting difficult.
In this talk, we qualitatively and quantitatively "explore" the geometric properties of this Hill model (slope and curvature) so as to be able to identify which samples can be fit reliably. In the field of statistical modeling, this "exploration" is called "domain knowledge", and this knowledge is then applied to obtain adequate fits on samples whose fits are uncertain.
This analysis should improve research efforts that aim to use mathematics and computers to develop better medicines and make the drug design process less expensive and time consuming. This talk should appeal to any student interested in mathematics and scientific inquiry.
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