Abstract
This paper presents an improved method, SlopeMiner, for analyzing time course microarray data by identifying genes that undergo gradual transitions in expression level. The algorithm calculates the slope for the slow transition between the expression levels of data, matching the sequence of expression level for each gene against temporal patterns having one transition between two expression levels. The method, when used along with StepMiner -an existing method for extracting binary signals, significantly increases the annotation accuracy.
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McCormick, K., Shrivastava, R., Liao, L. (2008). SlopeMiner: An Improved Method for Mining Subtle Signals in Time Course Microarray Data. In: Preparata, F.P., Wu, X., Yin, J. (eds) Frontiers in Algorithmics. FAW 2008. Lecture Notes in Computer Science, vol 5059. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-69311-6_6
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DOI: https://doi.org/10.1007/978-3-540-69311-6_6
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-69310-9
Online ISBN: 978-3-540-69311-6
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