SlopeMiner: An Improved Method for Mining Subtle Signals in Time Course Microarray Data

Authors:
Kevin Mccormick;Roli Shrivastava;Li Liao
Affiliations:
Computer and Information Sciences, University of Delaware, Newark, USA DE 19716;Computer and Information Sciences, University of Delaware, Newark, USA DE 19716;Computer and Information Sciences, University of Delaware, Newark, USA DE 19716
Venue:
FAW '08 Proceedings of the 2nd annual international workshop on Frontiers in Algorithmics
Year:
2008

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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.