Disease classification from capillary electrophoresis: mass spectrometry

Rogers, S., Girolami, M., Krebs, R. and Mischak, H. (2005) Disease classification from capillary electrophoresis: mass spectrometry. Lecture Notes in Computer Science, 3686, pp. 183-191. (doi:10.1007/11551188_20)

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Publisher's URL: http://dx.doi.org/10.1007/11551188_20

Abstract

We investigate the possibility of using pattern recognition techniques to classify various disease types using data produced by a new form of rapid Mass Spectrometry. The data format has several advantages over other high-throughput technologies and as such could become a useful diagnostic tool. We investigate the binary and multi-class performances obtained using standard classifiers as the number of features is varied and conclude that there is potential in this technique and suggest research directions that would improve performance.

Item Type:Articles
Status:Published
Refereed:Yes
Glasgow Author(s) Enlighten ID:Rogers, Dr Simon and Girolami, Prof Mark
Authors: Rogers, S., Girolami, M., Krebs, R., and Mischak, H.
Subjects:Q Science > QA Mathematics > QA75 Electronic computers. Computer science
College/School:College of Science and Engineering > School of Computing Science
Journal Name:Lecture Notes in Computer Science
Publisher:Springer
ISSN:1611-3349
Copyright Holders:Copyright © 2005 Springer
First Published:First published in Lecture Notes in Computer Science 3686:183-191
Publisher Policy:Reproduced in accordance with the copyright policy of the publisher.

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