TRICLUSTER: an effective algorithm for mining coherent clusters in 3D microarray data
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
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
Mining fuzzy frequent trends from time series
Expert Systems with Applications: An International Journal
Gene Specific Co-regulation Discovery: An Improved Approach
ICCS '09 Proceedings of the 9th International Conference on Computational Science: Part I
Cluster-based genetic segmentation of time series with DWT
Pattern Recognition Letters
Cluster analysis on time series gene expression data
International Journal of Business Intelligence and Data Mining
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Mining time-shifting co-regulation patterns from gene expression data
APWeb/WAIM'07 Proceedings of the joint 9th Asia-Pacific web and 8th international conference on web-age information management conference on Advances in data and web management
Efficiently mining time-delayed gene expression patterns
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Fuzzy data mining for time-series data
Applied Soft Computing
Mining biologically significant co-regulation patterns from microarray data
RSKT'06 Proceedings of the First international conference on Rough Sets and Knowledge Technology
Mining time-delayed coherent patterns in time series gene expression data
ADMA'06 Proceedings of the Second international conference on Advanced Data Mining and Applications
BiMine+: An efficient algorithm for discovering relevant biclusters of DNA microarray data
Knowledge-Based Systems
Discretization in gene expression data analysis: a selected survey
Proceedings of the Second International Conference on Computational Science, Engineering and Information Technology
ACSC '11 Proceedings of the Thirty-Fourth Australasian Computer Science Conference - Volume 113
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As the capture and analysis of single-time-point microarrayexpression data becomes routine, investigators are turningto time-series expression data to investigate complexgene regulation schemes and metabolic pathways. These investigationsare facilitated by algorithms that can extractand cluster related behaviors from the full population oftime-series behaviors observed. Although traditional clusteringtechniques have shown to be effective for certaintypes of expression analysis, they do not take the biologicalnature of the process into account, and therefore are clearlynot optimized for this purpose. Moreover, the current approachesprovide internal comparisons for the experimentsutilized for clustering, but cross-comparisons between clusteredresults are qualitative and subjective. We present acombination of current and novel methods for the analysisof time series gene expression data. We focus on an actualstudy we have performed for Haemophilus influenzaewhich is a major cause of otitis media in children. We firstperform a discretization of the gene expression data thattakes both positive and negative correlations into considerationand then develop a clustering algorithm optimizedfor such data that allows elucidation and searching of time-seriespatterns. The resulting approach allows time-seriesdata to be usefully compared across multiple experiments.We demonstrate the success of our algorithm by showingsome of the genes that it finds to be co-regulated are not detectedby current methods. As a result we are able to identifyseveral signal pathways that initiate competence development,and to characterize the transcriptomes of wild-typeand an adenylate cyclase mutant (cya) strains under bothnutrient-limiting and nutrient-complete growth conditions.