Enrichment of High-Throughput Screening Data with Increasing Levels of Noise Using Support Vector Machines, Recursive Partitioning, and Laplacian-Modified Naive Bayesian Classifiers

@article{Glick2006EnrichmentOH,
  title={Enrichment of High-Throughput Screening Data with Increasing Levels of Noise Using Support Vector Machines, Recursive Partitioning, and Laplacian-Modified Naive Bayesian Classifiers},
  author={Meir Glick and Jeremy L. Jenkins and James H. Nettles and Hamilton Hitchings and John W. Davies},
  journal={Journal of chemical information and modeling},
  year={2006},
  volume={46 1},
  pages={193-200}
}
High-throughput screening (HTS) plays a pivotal role in lead discovery for the pharmaceutical industry. In tandem, cheminformatics approaches are employed to increase the probability of the identification of novel biologically active compounds by mining the HTS data. HTS data is notoriously noisy, and therefore, the selection of the optimal data mining method is important for the success of such an analysis. Here, we describe a retrospective analysis of four HTS data sets using three mining… CONTINUE READING

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