Comparative Study of Machine-Learning and Chemometric Tools for Analysis of in-vivo High-Throughput Screening Data. Simmons, K., Kinney, J., Owens, A., Kleier, D., Bloch, K., Argentar, D., Walsh, A., & Vaidyanathan, G. J.~Chem.~Inf.~Model., 48(8):1663--1668, Aug, 2008.
doi  abstract   bibtex   
High-throughput screening (HTS) has become a central tool of many pharmaceutical and crop-protection discovery operations. If HTS screening is carried out at the level of the intact organism, as is commonly done in crop protection, this strategy has the potential of uncovering a completely new mechanism of actions. The challenge in running a cost-effective HTS operation is to identify ways in which to improve the overall success rate in discovering new biologically active compounds. To this end, we describe our efforts directed at making full use of the data stream arising from HTS. This paper describes a comparative study in which several machine learning and chemometric methodologies were used to develop classifiers on the same data sets derived from in vivo HTS campaigns and their predictive performances compared in terms of false negative and false positive error profiles.
@article{Simmons:2008kx,
	Abstract = {High-throughput screening (HTS) has become a central tool of many pharmaceutical and crop-protection discovery operations. If HTS screening is carried out at the level of the intact organism, as is commonly done in crop protection, this strategy has the potential of uncovering a completely new mechanism of actions. The challenge in running a cost-effective HTS operation is to identify ways in which to improve the overall success rate in discovering new biologically active compounds. To this end, we describe our efforts directed at making full use of the data stream arising from HTS. This paper describes a comparative study in which several machine learning and chemometric methodologies were used to develop classifiers on the same data sets derived from in vivo HTS campaigns and their predictive performances compared in terms of false negative and false positive error profiles.},
	Author = {Simmons, Kirk and Kinney, John and Owens, Aaron and Kleier, Dan and Bloch, Karen and Argentar, Dave and Walsh, Alicia and Vaidyanathan, Ganesh},
	Date-Added = {2011-11-07 10:36:44 -0500},
	Date-Modified = {2011-11-07 10:37:18 -0500},
	Doi = {10.1021/ci800142d},
	Journal = {J.~Chem.~Inf.~Model.},
	Journal-Full = {Journal of chemical information and modeling},
	Mesh = {Artificial Intelligence; Combinatorial Chemistry Techniques; Drug Evaluation, Preclinical; Models, Biological; Neural Networks (Computer)},
	Month = {Aug},
	Number = {8},
	Pages = {1663--1668},
	Pmid = {18681397},
	Pst = {ppublish},
	Title = {Comparative Study of Machine-Learning and Chemometric Tools for Analysis of in-vivo High-Throughput Screening Data},
	Volume = {48},
	Year = {2008},
	Bdsk-Url-1 = {http://dx.doi.org/10.1021/ci800142d}}

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