Abstract
We present a summary of a PhD thesis proposing efficient biclustering algorithms for time series gene expression data analysis, able to discover important aspects of gene regulation as anticorrelation and time-lagged relationships, and a scoring method based on statistical significance and similarity measures. The ability of the proposed algorithms to efficiently identify sets of genes with statistically significant and biologically meaningful expression patterns is shown to be instrumental in the discovery of relevant biological phenomena, leading to more convincing evidence of specific transcriptional regulatory mechanisms.
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Madeira, S.C., Oliveira, A.L. (2009). Efficient Biclustering Algorithms for Time Series Gene Expression Data Analysis. In: Omatu, S., et al. Distributed Computing, Artificial Intelligence, Bioinformatics, Soft Computing, and Ambient Assisted Living. IWANN 2009. Lecture Notes in Computer Science, vol 5518. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02481-8_154
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DOI: https://doi.org/10.1007/978-3-642-02481-8_154
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