title: Comparative study of machine-learning and chemometric tools for analysis of in-vivo high-throughput screening data.
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.
authors.affiliation: Simmons Consulting, 52 Windybush Way, Titusville, NJ 08560, USA. KirkASimmons@gmail.com
authors.forename: Kirk
authors.initials: K
authors.lastname: Simmons
authors.forename: John
authors.initials: J
authors.lastname: Kinney
authors.forename: Aaron
authors.initials: A
authors.lastname: Owens
authors.forename: Dan
authors.initials: D
authors.lastname: Kleier
authors.forename: Karen
authors.initials: K
authors.lastname: Bloch
authors.forename: Dave
authors.initials: D
authors.lastname: Argentar
authors.forename: Alicia
authors.initials: A
authors.lastname: Walsh
authors.forename: Ganesh
authors.initials: G
authors.lastname: Vaidyanathan
mesh_terms: D001185:Artificial Intelligence; D020650:Combinatorial Chemistry Techniques; D004353:Drug Evaluation, Preclinical; D008954:Models, Biological; D016571:Neural Networks, Computer