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Bioinformatics Advance Access published online on October 25, 2006
Bioinformatics, doi:10.1093/bioinformatics/btl545
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© The Author (2006). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org
Received August 14, 2006
Revised October 18, 2006
Accepted October 18, 2006

Article

Kalman filtering for disease-state estimation from microarray data

János Z. Kelemen 1 *, Attila Kertész-Farkas 2, András Kocsor 2, and László G. Puskás 1

1 Laboratory of Functional Genomics, Biological Research Centre, Hungarian Academy of Sciences, Szeged, Temesvári krt. 62, H-6726, Hungary
2 Research Group on Artificial Intelligence of the Hungarian Academy of Sciences and University of Szeged, Aradi vértanúk tere 1., H-6720 Szeged, Hungary

* To whom correspondence should be addressed.
János Z. Kelemen, E-mail: kelli{at}nucleus.szbk.u-szeged.hu


  Abstract

Motivation: In this paper we propose using the Kalman filter as a pre-processing step in microarray-based molecular diagnosis. Incorporating the expression covariance between genes is important in such classification problems, since this represents the functional relationships that govern tissue state. Failing to fulfil such requirements may result in biologically implausible class prediction models. Here we show that employing the Kalman filter to remove noise (while retaining meaningful covariance and thus being able to estimate the underlying biological state from microarray measurements) yields linearly separable data suitable for most classification algorithms.

Results: We demonstrate the utility and performance of the Kalman filter as a robust disease-state estimator on publicly available binary and multiclass microarray datasets in combination with the most widely used classification methods to date. Moreover, using popular graphical representation schemes we show that our filtered datasets also have an improved visualization capability.

Supplementary information: www.inf.u-szeged.hu/~kfa/kalman06/.

Associate Editor: Martin Bishop
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