Enrichment of High-Throughput Screening Data with Increasing Levels of Noise Using Support Vector Machines, Recursive Partitioning, and Laplacian-Modified Naive Bayesian Classifiers-Reference-Cited by-同舟云学术

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

Published:2005-12-03 Issue:1 Volume:46 Page:193-200
ISSN:1549-9596
Container-title:Journal of Chemical Information and Modeling
language:en
Short-container-title:J. Chem. Inf. Model.

Author:

Glick Meir¹,Jenkins Jeremy L.¹,Nettles James H.¹,Hitchings Hamilton¹,Davies John W.¹

Affiliation:

1. Lead Discovery Center, Novartis Institutes for Biomedical Research Inc., 250 Massachusetts Avenue, Cambridge, Massachusetts 02139, and Equbits LLC, 2625 Middlefield Road, #102, Palo Alto, California 94306

Publisher

American Chemical Society (ACS)

Subject

Library and Information Sciences,Computer Science Applications,General Chemical Engineering,General Chemistry

Link

https://pubs.acs.org/doi/pdf/10.1021/ci050374h

Reference32 articles.

1. Analysis of a Large Structure/Biological Activity Data Set Using Recursive Partitioning

2. Practical Approaches to Efficient Screening: Information-Rich Screening Protocol

3. Diversity screening versus focussed screening in drug discovery

4. Enrichment of Extremely Noisy High-Throughput Screening Data Using a Naïve Bayes Classifier

5. An Information-Theoretic Approach to Descriptor Selection for Database Profiling and QSAR Modeling

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