Classification of clinical gene-sample-time microarray expression data via tensor decomposition methods

Authors:
Yifeng Li;Alioune Ngom
Affiliations:
School of Computer Science, University of Windsor, Windsor, Ontario, Canada;School of Computer Science, University of Windsor, Windsor, Ontario, Canada
Venue:
CIBB'10 Proceedings of the 7th international conference on Computational intelligence methods for bioinformatics and biostatistics
Year:
2010

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Abstract

With the recent advances in microarray technology, the expression levels of genes with respect to samples can be monitored over a series of time points. Such three-dimensional microarray data, termed gene-sample-time (GST) microarray data, are gene expression matrices measured as a time-series. They have not yet received considerable attention, and analysis methods need to be devised specifically to tackle the complexity of GST datasets. We propose methods that are based on tensor decomposition for the sample classification. We use tensor decomposition in order to extract discriminative features as well as multilinearly reducing high dimensionality. We then classify the test samples in the reduced spaces. We have tested and compared our approaches on a real GST dataset. We show that our methods are at least comparable in prediction accuracy to recent methods devised for GST data. Most importantly, our methods run much faster than current approaches.