Multi-objective evolutionary biclustering of gene expression data
Pattern Recognition
A novel approach to revealing positive and negative co-regulated genes
Journal of Computer Science and Technology
Maximal Subspace Coregulated Gene Clustering
IEEE Transactions on Knowledge and Data Engineering
Gene interaction - An evolutionary biclustering approach
Information Fusion
Mean Square Residue Biclustering with Missing Data and Row Inversions
ISBRA '09 Proceedings of the 5th International Symposium on Bioinformatics Research and Applications
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Evolutionary biclustering with correlation for gene interaction networks
PReMI'07 Proceedings of the 2nd international conference on Pattern recognition and machine intelligence
Mean squared residue based biclustering algorithms
ISBRA'08 Proceedings of the 4th international conference on Bioinformatics research and applications
A Biologically Inspired Measure for Coexpression Analysis
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Mining biologically significant co-regulation patterns from microarray data
RSKT'06 Proceedings of the First international conference on Rough Sets and Knowledge Technology
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Although existing bicluster algorithms claimed to be able to discover co-regulated genes under a subset of given experiment conditions, they ignore the inherent sequential relationship between crucial time points and thus are not applicable to analyze time-series gene expression data. A simple and effective deletion-based algorithm, using the mean squared residue score as a measure, was developed to bicluster time-series gene expression data. While enforcing a threshold value for the score, the algorithm alternately eliminates genes and time points according to their correlation to the bicluster. To ensure the time locality, only the starting and ending points in the time interval are eligible for deletion. As a result, the number of genes and the length of time interval are simultaneously maximized. Our experimental results shown that the proposed method is capable of identifying co-regulated genes characterized by partial time-course data that previous methods failed to discover.