Efficient combinatorial drug optimization through stochastic search Conference Paper

abstract

• Multi-target therapeutics has been shown to be effective for treating complex diseases. In this paper, we propose a novel stochastic search algorithm that can be effectively used for combinatorial drug optimization. The proposed algorithm aims to enhance existing drug optimization, including the Gur Game algorithm, where the key of the proposed approach lies in utilizing a reference concentration to decide how to update a given drug combination to improve the drug response. We demonstrate that the proposed algorithm outperforms the existing algorithms, in terms of both efficiency and success rate. 2011 IEEE.

authors

• Yoon, Byung-Jun

published proceedings

• Proceedings - IEEE International Workshop on Genomic Signal Processing and Statistics

author list (cited authors)

• Kim, M., & Yoon, B. J.

citation count

• 1

complete list of authors

• Kim, M||Yoon, BJ

publication date

• December 2011

published in

• IEEE International Workshop on Genomic Signal Processing and Statistics : [proceedings]. IEEE International Workshop on Genomic Signal Processing and Statistics  Journal

Digital Object Identifier (DOI)

• 10.1109/gensips.2011.6169434

International Standard Book Number (ISBN) 13

• 9781467304900

start page

• 33

end page

• 35

URL

• https://doi.org/10.1109/gensips.2011.6169434