BioMed Research International

BioMed Research International / 2005 / Article
Special Issue

Data Mining in Genomics and Proteomics

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Research article | Open Access

Volume 2005 |Article ID 390462 | https://doi.org/10.1155/JBB.2005.80

Halima Bensmail, Buddana Aruna, O. John Semmes, Abdelali Haoudi, "Functional Clustering Algorithm for High-Dimensional Proteomics Data", BioMed Research International, vol. 2005, Article ID 390462, 7 pages, 2005. https://doi.org/10.1155/JBB.2005.80

Functional Clustering Algorithm for High-Dimensional Proteomics Data

Received09 Sep 2004
Revised10 Feb 2005
Accepted14 Feb 2005

Abstract

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.

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.


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