αCORR: a novel algorithm for clustering gene expression data

Recent advances in biotechnology allow researchers to measure expression levels for thousands of genes simultaneously, across different conditions and over time. Analysis of data produced by such experiments offers potential insight into gene function and regulatory mechanisms. A key step in the analysis of gene expression data is the detection of groups of genes that manifest similar expression patterns. The corresponding algorithmic problem is to cluster multi-condition gene expression patterns. This paper aims to introduce a new clustering algorithm for gene expression data. The design of the proposed algorithm tries to avoid some of the drawbacks and the disadvantages of the present algorithms of clustering gene expression data. The proposed αCORRclustering algorithm is tested and verified on real biological data sets.