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Microarray time-series data classification via multiple alignment of gene expression profiles
datasetposted on 2017-11-21, 00:21 authored by Bari, Ataul, Rueda, Luis, Ngom, Alioune
Pairwise alignment approaches for time-varying gene expression profiles have been recently developed for the detection of co-expressions in time-series microarray data sets. In this paper, we analyze multiple expression profile alignment (MEPA) methods for classifying microarray time-course data. We apply a nearest centroid classification technique, in which the centroid of each class is computed by means of a MEPA algorithm. MEPA aligns the expression profiles in such a way to minimize the total area between all aligned profiles. We propose four MEPA approaches whose effectiveness are demonstrated on the well-known budding yeast, S. cerevisiae, data set. PRIB 2008 proceedings found at: http://dx.doi.org/10.1007/978-3-540-88436-1 Contributors: Monash University. Faculty of Information Technology. Gippsland School of Information Technology ; Chetty, Madhu ; Ahmad, Shandar ; Ngom, Alioune ; Teng, Shyh Wei ; Third IAPR International Conference on Pattern Recognition in Bioinformatics (PRIB) (3rd : 2008 : Melbourne, Australia) ; Coverage: Rights: Copyright by Third IAPR International Conference on Pattern Recognition in Bioinformatics. All rights reserved.
Bioinformatics -- CongressesComputational biology -- CongressesComputer vision in medicine -- CongressesComputational biology -- Methods -- CongressesPattern recognition, automated -- Methods -- Congresses2008conference paper1959.1/63682monash:7849Bioinformatics SoftwareBioinformaticsPattern Recognition and Data Mining