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Bioinformatics Advance Access originally published online on October 25, 2006
Bioinformatics 2006 22(24):3047-3053; 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

Kalman filtering for disease-state estimation from microarray data

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

Laboratory of Functional Genomics, Biological Research Centre, Hungarian Academy of Sciences, Szeged Temesvári krt. 62, H-6726, Hungary
1 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.

Motivation: In this paper, we propose using the Kalman filter (KF) 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 KF 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 KF as a robust disease-state estimator on publicly available binary and multi-class 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.

Contact: kelli{at}nucleus.szbk.u-szeged.hu.

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

Received on August 14, 2006; revised on October 18, 2006; accepted on October 18, 2006

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