Self-organization and associative memory: 3rd edition
Self-organization and associative memory: 3rd edition
OPTICS: ordering points to identify the clustering structure
SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
A clustering algorithm based on graph connectivity
Information Processing Letters
Clustering by pattern similarity in large data sets
Proceedings of the 2002 ACM SIGMOD international conference on Management of data
A new approach to analyzing gene expression time series data
Proceedings of the sixth annual international conference on Computational biology
Biclustering of Expression Data
Proceedings of the Eighth International Conference on Intelligent Systems for Molecular Biology
Center CLICK: A Clustering Algorithm with Applications to Gene Expression Analysis
Proceedings of the Eighth International Conference on Intelligent Systems for Molecular Biology
DHC: A Density-Based Hierarchical Clustering Method for Time Series Gene Expression Data
BIBE '03 Proceedings of the 3rd IEEE Symposium on BioInformatics and BioEngineering
d-Clusters: Capturing Subspace Correlation in a Large Data Set
ICDE '02 Proceedings of the 18th International Conference on Data Engineering
MaPle: A Fast Algorithm for Maximal Pattern-based Clustering
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Interactive exploration of coherent patterns in time-series gene expression data
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
GPX: interactive mining of gene expression data
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
Data analysis and bioinformatics
PReMI'07 Proceedings of the 2nd international conference on Pattern recognition and machine intelligence
The ParTriCluster algorithm for gene expression analysis
International Journal of Parallel Programming
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Analyzing coherent gene expression patterns is an important task in bioinformatics research and biomedical applications. Recently, various clustering methods have been adapted or proposed to identify clusters of co-expressed genes and recognize coherent expression patterns as the centroids of the clusters. However, the interpretation of co-expressed genes and coherent patterns mainly depends on the domain knowledge, which presents several challenges for coherent pattern mining and cannot be solved by most existing clustering approaches.In this paper, we introduce an interactive exploration system GeneX (Gene eXplorer) for mining coherent expression patterns. We develop a novel coherent pattern index graph to provide highly confident indications of the existence of coherent patterns. Typical exploration operations are supported based on the index graph. We also provide a bunch of graphical views as the user interface to visualize the data set and facilitate the interactive operations. To help users to interpret and validate the mining results, we design the gene annotation panel that connects the genes with some public annotation databases. The experimental results show that our approach is more effective than the state-of-the-art methods in mining real gene expression data sets.