Enhanced pClustering and Its Applications to Gene Expression Data

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BIBE '04 Proceedings of the 4th IEEE Symposium on Bioinformatics and Bioengineering
Year:
2004

Citing 0
Cited 2

Discovering Coherent Biclusters from Gene Expression Data Using Zero-Suppressed Binary Decision Diagrams

IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Joint co-clustering: Co-clustering of genomic and clinical bioimaging data

Computers & Mathematics with Applications

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Abstract

Clustering has been one of the most popular methodsto discover useful biological insights from DNA microarray.An interesting paradigm is simultaneous clustering ofboth genes and experiments. This "biclustering" paradigmaims at discovering clusters that consist of a subset of thegenes showing a coherent expression pattern over a subsetof conditions. The pClustering approach is a techniquethat belongs to this paradigm. Despite many theoretical advantages,this technique has been rarely applied to actualgene expression data analysis. Possible reasons include theworst-case complexity of the clustering algorithm and thedifficulty in interpreting clustering results. In this paper, wepropose an enhanced framework for performing pClusteringon actual gene expression analysis. Our new frameworkincludes an effective data preparation method, highly scalableclustering strategies, and an intuitive result interpretationscheme. The experimental result confirms the effectivenessof our approach.