2nd International ICST Conference on Bio-Inspired Models of Network, Information, and Computing Systems

Research Article

Clustering Temporal Gene Expression Data with Unequal Time Intervals

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  • @INPROCEEDINGS{10.4108/ICST.BIONETICS2007.2428,
        author={Luis Rueda and Ataul Bari},
        title={Clustering Temporal Gene Expression Data with Unequal Time Intervals},
        proceedings={2nd International ICST Conference on Bio-Inspired Models of Network, Information, and Computing Systems},
        proceedings_a={BIONETICS},
        year={2008},
        month={8},
        keywords={Gene expression  clustering  time-series profiling},
        doi={10.4108/ICST.BIONETICS2007.2428}
    }
    
  • Luis Rueda
    Ataul Bari
    Year: 2008
    Clustering Temporal Gene Expression Data with Unequal Time Intervals
    BIONETICS
    ICST
    DOI: 10.4108/ICST.BIONETICS2007.2428
Luis Rueda1,*, Ataul Bari2,*
  • 1: Department of Computer Science University of Concepción, Edmundo Larenas 215 Concepción, 4030000, Chile
  • 2: School of Computer Science University of Windsor, 401 Sunset Avenue Windsor, ON, N9B 3P4, Canada
*Contact email: lrueda@inf.udec.cl, bari1@uwindsor.ca

Abstract

We have focused on the problem of clustering time-series gene expression data. We present a novel algorithm for clustering gene temporal expression profile microarray data, which is fairly simple but powerful enough to find an efficient distribution of genes over clusters. Using a variant of a clustering index can effectively decide upon the optimal number of clusters for a given dataset. The clustering method is based on a profile-alignment approach, which we propose and that minimizes the (square) area between two aligned vector profiles, to hierarchically cluster microarray time series data. The effectiveness of the proposed approach is demonstrated on two well-known, yeast and serum.