Analysis and visualization of gene expression data using Self-Organizing Maps

@article{Nikkil2001AnalysisAV,
  title={Analysis and visualization of gene expression data using Self-Organizing Maps},
  author={Janne Nikkil{\"a} and Petri T{\"o}r{\"o}nen and Samuel Kaski and Jarkko Venna and Eero Castr{\'e}n and Garry Wong},
  journal={Neural networks : the official journal of the International Neural Network Society},
  year={2001},
  volume={15 8-9},
  pages={
          953-66
        }
}
Cluster structure of gene expression data obtained from DNA microarrays is analyzed and visualized with the Self-Organizing Map (SOM) algorithm. The SOM forms a non-linear mapping of the data to a two-dimensional map grid that can be used as an exploratory data analysis tool for generating hypotheses on the relationships, and ultimately of the function of the genes. Similarity relationships within the data and cluster structures can be visualized and interpreted. The methods are demonstrated by… CONTINUE READING
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