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Stanford, California
Aug. 8, 2005 to Aug. 11, 2005
ISBN: 0-7695-2442-7
pp: 97-98
Ammaiappan Balasubramanian , Siemens Corporate Research
Amit Chakraborty , Siemens Corporate Research
Dorin Comaniciu , Siemens Corporate Research
ABSTRACT
<p>DNA microarray experiments generate a substantial amount of information about global gene expression. Gene expression profiles can be represented as points in multi-dimensional space. It is essential to identify relevant groups of genes in biomedical research. Clustering is helpful in pattern recognition in gene expression profiles. Some clustering techniques have been introduced. However, these traditional methods mainly utilize shape-based assumption or distance metric to cluster the points in multi-dimension linear Euclidean space. Poor consistence with the functional annotation of genes is shown in their validation study.</p> <p>We propose fractal clustering method to cluster genes using intrinsic (fractal) dimension from modern geometry. Fractal dimension is used to characterize the degree of self-similarity among the points in the clusters. The main idea of fractal clustering is to group points in a cluster in such a way that none of the points in the cluster changes the cluster?s intrinsic dimension radically. We computed Hausdorff fractal dimension through the means of the box-counting plot algorithm, since it is the fastest and also robust enough.</p> <p>We assess this method using validation assessment using public microarray dataset. It shows that this method is superior in identifying functional related gene groups than other traditional methods.</p>
INDEX TERMS
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CITATION
Ammaiappan Balasubramanian, Amit Chakraborty, Dorin Comaniciu, "Fractal Clustering for Microarray Data Analysis", CSBW, 2005, 2005 IEEE Computational Systems Bioinformatics Conference - Workshops, 2005 IEEE Computational Systems Bioinformatics Conference - Workshops 2005, pp. 97-98, doi:10.1109/CSBW.2005.66
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