@inproceedings{cf88fe3ef70a4af693d8c3e0613a2c0b,
title = "Clustering microarray time-series data using expectation maximization and multiple profile alignment",
abstract = "A common problem in biology is to partition a set of experimental data into clusters in such a way that the data points within the same cluster are highly similar while data points in different clusters are very different. In this direction, clustering microarray time-series data via pairwise alignment of piece-wise linear profiles has been recently introduced. We propose a EM 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 yield encouraging results with 83.26% accuracy.",
keywords = "Clustering, Cubic spline, Gene expression profiles, Microarrays, Profile alignment, Time-series data",
author = "Numanul Subhani and Luis Rueda and Alioune Ngom and Burden, {Conrad J.}",
year = "2009",
doi = "10.1109/BIBMW.2009.5332128",
language = "English",
isbn = "9781424451210",
series = "Proceedings - 2009 IEEE International Conference on Bioinformatics and Biomedicine Workshops, BIBMW 2009",
pages = "2--7",
booktitle = "Proceedings - 2009 IEEE International Conference on Bioinformatics and Biomedicine Workshops, BIBMW 2009",
note = "2009 IEEE International Conference on Bioinformatics and Biomedicine Workshops, BIBMW 2009 ; Conference date: 01-11-2009 Through 04-11-2009",
}