Tanimoto Metric in Tree-SOM for Improved Representation of Mass Spectrometry Data with an Underlying Taxonomic Structure

In this paper, we develop a Tanimoto metric variant of the evolving tree for the analysis of mass spectrometric data of animal fur. The evolving tree is an extension of self-organizing maps developed to analyze hierarchical clustering problems. Together with the Tanimoto similarity measure, which is intended to work with taxonomic structured data, the evolving tree is well suited for the identification of animal hair based on mass spectrometry fingerprints. Results show a suitable hierarchical clustering of the test data and also a good retrieval capability with a logarithmic number of comparisons.