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Clustering problems in gene data analysis

The information obtained by monitoring gene expression levels in different developmental stages, tissue types, clinical conditions and different organism can help in understanding gene function and gene networks, assist in the diagnostic of disease conditions and reveal the effects of medical treatments.A key step in the analysis of gene expression data is the identification of groups of genes that manifest similar expression patterns. This translates to the algorithmic problem of clustering gene expression data.The challenges relate to the large quantity of data to be analyzed, which makes the clustering algorithms extremely computational expensive. Also, there is no assumption on the number of clusters or their structure, which complicates furthermore the problem.The goal of this project is to modify and adapt a category of graph theoretical methods (NCUT) to be used in clustering gene data. Ways to improve the speed of the algorithms are also to be investigated.