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    STUDIA INFORMATICA - Issue no. 1 / 2014  
         
  Article:   A STUDY ON DYNAMIC CLUSTERING OF GENE EXPRESSION DATA .

Authors:  . 		 
       
         
  Abstract:   Microarray and next-generation sequencing technologies allow measuring the levels of expressions of thousands of genes simultaneously. One of the most popular procedures used to analyze gene expression data is clustering. To study biological processes which evolve over time, researchers can either perform reclustering from scratch every time new gene expression levels are available, which would be very time consuming, or adapt the previously obtained partitions using a dynamic clustering algorithm. This paper aims to investigate a couple of heuristics for centroids identi fication within a dynamic k-means based clustering algorithm that was previously introduced for clustering of gene expression data. Computational experiments on a real-life gene expression data set are provided, as well as an analysis of the obtained results.

2010 Mathematics Subject Classi fication. 68P15, 68T05. 1998 CR Categories and Descriptors. I.2.6 [Computing Methodologies]: Arti ficial Intelligence - Learning; I.2.8 [Computing Methodologies]: Problem Solving, Control Methods, and Search - Heuristic methods.

Key words and phrases. Bioinformatics, Dynamic clustering.
 
         
     
         
         
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