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Journal of Biomedicine and Biotechnology - Volume 2005 2005, Issue 2, Pages 80-86

Research article

Department of Statistic Operation and Management Sciences SOMS, The University of Tennessee, Knoxville, TN 37996, USA

Department of Microbiology and Molecular Cell Biology, Eastern Virginia Medical School, Norfolk, VA 23507, USA

Received 9 September 2004; Revised 10 February 2005; Accepted 14 February 2005

Copyright © 2005 Hindawi Publishing Corporation. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Clustering proteomics data is a challenging problem for any traditional clustering algorithm. Usually, the number of samples is largely smaller than the number of protein peaks. The use of a clustering algorithm which does not take into consideration the number of features of variables here the number of peaks is needed. An innovative hierarchical clustering algorithm may be a good approach. We propose here a new dissimilarity measure for the hierarchical clustering combined with a functional data analysis. We present a specific application of functional data analysis FDA to a high-throughput proteomics study. The high performance of the proposed algorithm is compared to two popular dissimilarity measures in the clustering of normal and human T-cell leukemia virus type 1 HTLV-1-infected patients samples.

Autor: Halima Bensmail, Buddana Aruna, O. John Semmes, and Abdelali Haoudi



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