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