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Chu, C.W., Holliday, J.D. and Willett, P. (2012) Combining multiple classifications of chemical structures using consensus clustering. Bioorganic & Medicinal Chemistry Letters, 20 (18). 5366 - 5371.
Abstract
Consensus clustering involves combining multiple clusterings of the same set of objects to achieve a single clustering that will, hopefully, provide a better picture of the groupings that are present in a dataset. This paper reports the use of consensus clustering methods on sets of chemical compounds represented by 2D fingerprints. Experiments with DUD, IDAlert, MDDR and MUV data suggests that consensus methods are unlikely to result in significant improvements in clustering effectiveness as compared to the use of a single clustering method.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2012 Elsevier. This is an author produced version of a paper subsequently published in Bioorganic & Medicinal Chemistry. Uploaded in accordance with the publisher's self-archiving policy. |
Keywords: | Cluster analysis; Consensus clustering; Fingerprint; Group-average clustering method; k-Means clustering method |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Social Sciences (Sheffield) > Information School (Sheffield) |
Depositing User: | Miss Anthea Tucker |
Date Deposited: | 04 Mar 2014 10:04 |
Last Modified: | 04 Mar 2014 10:04 |
Published Version: | http://dx.doi.org/10.1016/j.bmc.2012.03.010 |
Status: | Published |
Publisher: | Elsevier |
Refereed: | Yes |
Identification Number: | 10.1016/j.bmc.2012.03.010 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:77366 |
Available Versions of this Item
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Combining multiple classifications of chemical structures using consensus clustering. (deposited 09 Jan 2013 15:37)
- Combining multiple classifications of chemical structures using consensus clustering. (deposited 04 Mar 2014 10:04) [Currently Displayed]