Abstract
The ability to monitor the change in expression patterns over time, and to observe the emergence of coherent temporal responses using gene expression time series, obtained from either microarray or RNAseq technologies, is critical to advance our understanding of complex biomedical processes such as growth, development, response to stimulus, disease progression and drug responses. In this paper, we propose parallel e-CCC-Biclustering, a parallel version of the state of the art e-CCC-Biclustering algorithm, an efficient exhaustive search biclustering algorithmto mine approximate temporal expression patterns. Parallel e-CCC-Biclustering implemented using functional programming and achieved a super-linear speed-up when compared to the original sequential algorithm in test cases using synthetic data.
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© 2012 Springer-Verlag Berlin Heidelberg
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Cristóvão, F., Madeira, S.C. (2012). Parallel e-CCC-Biclustering: Mining Approximate Temporal Patterns in Gene Expression Time Series Using Parallel Biclustering. In: Rocha, M., Luscombe, N., Fdez-Riverola, F., Rodríguez, J. (eds) 6th International Conference on Practical Applications of Computational Biology & Bioinformatics. Advances in Intelligent and Soft Computing, vol 154. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-28839-5_3
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DOI: https://doi.org/10.1007/978-3-642-28839-5_3
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-28838-8
Online ISBN: 978-3-642-28839-5
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