IEEE Statistical Signal Processing Workshop,
accepted for publication.
Adaptive Experimental Design for Drug Combinations
Brian L. Evans and
Department of Electrical
and Computer Engineering,
Wireless Networking and Communications Group,
The University of Texas at Austin,
Austin, TX 78712 USA
Paper Draft -
Drug cocktails formed by mixing multiple drugs at various
doses provide more effective cures than single-drug treatments.
However, drugs interact in highly nonlinear ways making the
determination of the optimal combination a difficult task.
The response surface of the drug cocktail has to be estimated
through expensive and time-consuming experimentation.
Previous research focused on the use of spaceexploratory
heuristics such as genetic algorithms to guide the
search for optimal combinations.
While being more efficient than random sampling, these methods
require a considerable amount of experiments to converge to
In this paper, we propose to use an information-theoretic
active learning approach under the Bayesian framework of
Gaussian processes to adaptively choose what experiments
to perform based on current data points.
We show that our approach is able to reduce the number of
required data points significantly.
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Last Updated 08/07/12.