Parallel e-CCC-Biclustering: Mining Approximate Temporal Patterns in Gene Expression Time Series Using Parallel Biclustering

Conference paper
Part of the Advances in Intelligent and Soft Computing book series (AINSC, volume 154)

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.

Keywords

Parallel Algorithm Parallel Version Functional Programming Father Model Multicore Architecture 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Amdahl, G.M.: Validity of the single processor approach to achieving large scale computing capabilities. In: Proceedings of the Spring Joint Computer Conference, pp. 483–485 (1967)Google Scholar
  2. 2.
    Gustafson, J.L.: Reevaluating amdahl’s law. Communications of the ACM 31, 532–533 (1988)CrossRefGoogle Scholar
  3. 3.
    Madeira, S., Oliveira, A.: A polynomial time biclustering algorithm for finding approximate expression patterns in gene expression time series. Algorithms for Molecular Biology 4(1), 8 (2009)CrossRefGoogle Scholar
  4. 4.
    Mejia-Roa, E., Garcia, C., Gomez, J., Prieto, M., Nogales-Cadenas, R., Tirado, F., Pascual-Montano, A.: Biclustering and classification analysis in gene expression using non-negative matrix factorization on multi-gpu systems. In: 11th International Conference on Intelligent Systems Design and Applications, ISDA (2011)Google Scholar
  5. 5.
    Odersky, M.: The Scala Language Specification, version 2.7 (2009)Google Scholar
  6. 6.
    Tewfik, A., Tchagang, A., Vertatschitsch, L.: Parallel identification of gene biclusters with coherent evolutions. IEEE Transactions on Signal Processing 54(6), 2408–2417 (2006)CrossRefGoogle Scholar
  7. 7.
    Zhou, J., Khokhar, A.: Parrescue: Scalable parallel algorithm and implementation for biclustering over large distributed datasets. In: 26th IEEE International Conference on Distributed Computing Systems (2006)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  1. 1.Software Engineering Group (ESW), INESC-ID, Lisbon, and Instituto Superior TécnicoTechnical University of LisbonLisbonPortugal
  2. 2.Knowledge Discovery and Bioinformatics (KDBIO) group, INESC-ID, Lisbon, and Instituto Superior TécnicoTechnical University of LisbonLisbonPortugal

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