@inproceedings{f9ecdcf2e45044f2a1cc66c11ff520fe,
title = "Microarray time-series data clustering via multiple alignment of gene expression profiles",
abstract = "Genes with similar expression profiles are expected to be functionally related or co-regulated. In this direction, clustering microarray time-series data via pairwise alignment of piece-wise linear profiles has been recently introduced. We propose a k-means clustering approach based on a multiple alignment of natural cubic spline representations of gene expression profiles. The multiple alignment is achieved by minimizing the sum of integrated squared errors over a time-interval, defined on a set of profiles. Preliminary experiments on a well-known data set of 221 pre-clustered Saccharomyces cerevisiae gene expression profiles yields excellent results with 79.64% accuracy.",
keywords = "Cubic Spline, Gene Expression Profiles, K-Means Clustering, Microarrays, Profile Alignment, Time-Series Data",
author = "Numanul Subhani and Alioune Ngom and Luis Rueda and Conrad Burden",
year = "2009",
doi = "10.1007/978-3-642-04031-3_33",
language = "English",
isbn = "3642040306",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
pages = "377--390",
booktitle = "Pattern Recognition in Bioinformatics - 4th IAPR International Conference, PRIB 2009, Proceedings",
note = "4th IAPR International Conference on Pattern Recognition in Bioinformatics, PRIB 2009 ; Conference date: 07-09-2009 Through 09-09-2009",
}