Microarray time-series data classification via multiple alignment of gene expression profiles Bari, Ataul Rueda, Luis Ngom, Alioune 10.4225/03/5a1371a04a06e https://bridges.monash.edu/articles/dataset/Microarray_time-series_data_classification_via_multiple_alignment_of_gene_expression_profiles/5619469 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. 2017-11-21 00:21:50 Bioinformatics -- Congresses Computational biology -- Congresses Computer vision in medicine -- Congresses Computational biology -- Methods -- Congresses Pattern recognition, automated -- Methods -- Congresses 2008 conference paper 1959.1/63682 monash:7849 Bioinformatics Software Bioinformatics Pattern Recognition and Data Mining