Silhouettes: a graphical aid to the interpretation and validation of cluster analysis
Journal of Computational and Applied Mathematics
Algorithms for clustering data
Algorithms for clustering data
Bayesian Clustering by Dynamics
Machine Learning - Special issue: Unsupervised learning
An Information-Theoretic Definition of Similarity
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Center CLICK: A Clustering Algorithm with Applications to Gene Expression Analysis
Proceedings of the Eighth International Conference on Intelligent Systems for Molecular Biology
Clustering short time series gene expression data
Bioinformatics
Using information content to evaluate semantic similarity in a taxonomy
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1
Data mining of gene expression changes in Alzheimer brain
Artificial Intelligence in Medicine
Goal driven analysis of cDNA microarray data
CIBCB'09 Proceedings of the 6th Annual IEEE conference on Computational Intelligence in Bioinformatics and Computational Biology
Integrative data mining in functional genomics of brassica napus and arabidopsis thaliana
IEA/AIE'10 Proceedings of the 23rd international conference on Industrial engineering and other applications of applied intelligent systems - Volume Part III
Merge method for shape-based clustering in time series microarray analysis
IDEAL'12 Proceedings of the 13th international conference on Intelligent Data Engineering and Automated Learning
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Identification of co-expressed genes sharing similar biological behaviours is an essential step in functional genomics. Traditional clustering techniques are generally based on overall similarity of expression levels and often generate clusters with mixed profile patterns. A novel pattern recognition method for selecting co-expressed genes based on rate of change and modulation status of gene expression at each time interval is proposed in this paper. This method is capable of identifying gene clusters consisting of highly similar shapes of expression profiles and modulation patterns. Furthermore, we develop a quality index based on the semantic similarity in gene annotations to assess the likelihood of a cluster being a co-regulated group. The effectiveness of the proposed methodology is demonstrated by applying it to the well-known yeast sporulation dataset and an in-house cancer genomics dataset.