An unsupervised hierarchical dynamic self-organizing approach to cancer class discovery and marker gene identification in microarray data

@article{Hsu2003AnUH,
  title={An unsupervised hierarchical dynamic self-organizing approach to cancer class discovery and marker gene identification in microarray data},
  author={Arthur L. Hsu and Sen-Lin Tang and Saman K. Halgamuge},
  journal={Bioinformatics},
  year={2003},
  volume={19 16},
  pages={2131-40}
}
MOTIVATION Current Self-Organizing Maps (SOMs) approaches to gene expression pattern clustering require the user to predefine the number of clusters likely to be expected. Hierarchical clustering methods used in this area do not provide unique partitioning of data. We describe an unsupervised dynamic hierarchical self-organizing approach, which suggests an appropriate number of clusters, to perform class discovery and marker gene identification in microarray data. In the process of class… CONTINUE READING
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