Functional Clustering Algorithm for High-Dimensional Proteomics Data Article

Open Access

author list

  • Bensmail, H; Aruna, B; Semmes, OJ; Haoudi, A


  • 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 type1(HTLV-1)-infected patients samples.


publication date

  • 2005


number of pages

  • 6

start page

  • 80

end page

  • 86


  • 2005


  • 2