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

  • Filipe Cristóvão
  • Sara C. Madeira
Conference paper
Part of the Advances in Intelligent and Soft Computing book series (AINSC, volume 154)


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.


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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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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