Comparative study of machine-learning and chemometric tools for analysis of in-vivo high-throughput screening data.


Journal Article

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.

Full Text

Duke Authors

Cited Authors

  • Simmons, K; Kinney, J; Owens, A; Kleier, D; Bloch, K; Argentar, D; Walsh, A; Vaidyanathan, G

Published Date

  • August 2008

Published In

Volume / Issue

  • 48 / 8

Start / End Page

  • 1663 - 1668

PubMed ID

  • 18681397

Pubmed Central ID

  • 18681397

International Standard Serial Number (ISSN)

  • 1549-9596

Digital Object Identifier (DOI)

  • 10.1021/ci800142d


  • eng

Conference Location

  • United States