NDLI: A sensitivity analysis of microarray feature selection and classification under measurement noise

Content Provider	IEEE Xplore Digital Library
Author	Sontrop, H. van den Ham, R. Moerland, P. Reinders, M. Verhaegh, W.
Copyright Year	2009
Description	Author affiliation: Delft University of Technology, Mekelweg 4, 2628 CD, The Netherlands (Reinders, M.) \|\| Philips Research, High Tech Campus 12a, 5656 AE Eindhoven, The Netherlands (Sontrop, H.; van den Ham, R.; Verhaegh, W.) \|\| Academic Medical Center, Meibergdreef 9, 1100 AZ Amsterdam, The Netherlands (Moerland, P.)
Abstract	Microarray experiments typically generate data with a fairly high level of technical noise. Whereas this noise information is sometimes used in tests for differential expression and in clustering tasks, its effect on classification has remained underexposed. In this paper we assess the stability of microarray feature selection and classification under measurement noise. We do so by repeating the experiments many times, using perturbed expression measurements, based on reported uncertainty information. For a well-known study from the literature, the experiments show that the feature selection outcome can vary considerably, and that classification is quite unstable for 7 out of the 106 validation samples, in the sense that over 25% of the perturbations are not assigned to their original class. We also show that classification stability decreases when fewer genes are selected.
Starting Page	1
Ending Page	4
File Size	1793457
Page Count	4
File Format	PDF
ISBN	9781424447619
DOI	10.1109/GENSIPS.2009.5174352
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-05-17
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Sensitivity analysis Noise measurement
Content Type	Text
Resource Type	Article

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