Combination of a naive Bayes classifier with consensus scoring improves enrichment of high-throughput docking results.
Tools
Tools
Klon, Anthony, Glick, Meir and Davies, John (2004) Combination of a naive Bayes classifier with consensus scoring improves enrichment of high-throughput docking results. Journal of Medicinal Chemistry, 47 (18). pp. 4356-4359. ISSN 0022-2623
Abstract
We have previously shown that a machine learning technique can improve the enrichment of high-throughput docking (HTD) results. In the previous cases studied, however, the application of a naive Bayes classifier failed to improve enrichment for instances where HTD alone was unable to generate an acceptable enrichment. We present here a protocol to rescue poor docking results a priori using a combination of rank-by-median consensus scoring and naive Bayesian categorization.
| Item Type: | Article |
|---|---|
| Related URLs: | |
| Additional Information: | archiving not formally supported by publisher |
| Related URLs: | |
| Date Deposited: | 14 Dec 2009 14:07 |
| Last Modified: | 31 Jan 2013 01:31 |
| URI: | https://oak.novartis.com/id/eprint/45 |