Algorithms on strings, trees, and sequences: computer science and computational biology
Algorithms on strings, trees, and sequences: computer science and computational biology
The maximum edge biclique problem is NP-complete
Discrete Applied Mathematics
Biclustering Algorithms for Biological Data Analysis: A Survey
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Efficient Biclustering Algorithms for Time Series Gene Expression Data Analysis
IWANN '09 Proceedings of the 10th International Work-Conference on Artificial Neural Networks: Part II: Distributed Computing, Artificial Intelligence, Bioinformatics, Soft Computing, and Ambient Assisted Living
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
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Identifying patterns in temporal data supports complex analyses in several domains, including stock markets (finance) and social interactions (social science). Clinical and biological applications, such as monitoring patient response to treatment or characterizing activity at the molecular level, are also of interest. In particular, researchers seek to gain insight into the dynamics of biological processes, and potential perturbations of these leading to disease, through the discovery of patterns in time series gene expression data. For many years, clustering has remained the standard technique to group genes exhibiting similar response profiles. However, clustering defines similarity across all time points, focusing on global patterns which tend to characterize rather broad and unspecific responses. It is widely believed that local patterns offer additional insight into the underlying intricate events leading to the overall observed behavior. Efficient biclustering algorithms have been devised for the discovery of temporally aligned local patterns in gene expression time series, but the extraction of time-lagged patterns remains a challenge due to the combinatorial explosion of pattern occurrence combinations when delays are considered. We present heuristic approaches enabling polynomial rather than exponential time solutions for the problem.