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Semi-Automated Clustering of Gene Expression Data Sets

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6 Author(s)
Minho Kim ; Electron. & Telecommun. Res. Inst. (ETRI), Daejeon ; Ho-Youl Jung ; Myungguen Chung ; Pora Kim
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Clustering, as one of key analysis tools for gene expression data sets, attempts to discover groups of genes having similar expression patterns. In order to get a reasonable biological interpretation, it is desirable that a clustering result be accurate enough. However, conventional clustering methods do not always meet this demand since they require the exact tuning of input parameters and cluster centers for an acceptable quality of result. Through an intuitive user interaction, Ul-Cluster solves the problem mentioned above, and yields better clustering results.

Published in:

Engineering in Medicine and Biology Society, 2007. EMBS 2007. 29th Annual International Conference of the IEEE

Date of Conference:

22-26 Aug. 2007