Functional clustering algorithm for high-dimensional proteomics data

Halima Bensmail, Buddana Aruna, O. John Semmes, Abdelali Haoudi

Research output: Contribution to journalArticle

7 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)80-86
Number of pages7
JournalJournal of Biomedicine and Biotechnology
Volume2005
Issue number2
DOIs
Publication statusPublished - 30 Jun 2005

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ASJC Scopus subject areas

  • Biotechnology
  • Molecular Medicine
  • Molecular Biology
  • Genetics
  • Health, Toxicology and Mutagenesis

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