Dr. Sumeet Dua

Max P. and Robbie L. Watson Eminent Scholar Chair

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Padma P. Korimilli (2006)

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Quad-tree Based Approach for Bi-clustering of Gene Expression Data; MS-CS Thesis, Student: Padma P. Korimilli (2006).

Advances in Bioinformatics can be mainly attributed to the ability of microarrays to rapidly and accurately monitor transcriptional behavior over a whole genome under different conditions. Over the years, clustering techniques have played a major role in microarray data analysis in discovering groups of genes that share similar transcriptional behavior over the conditions in microarray experiments.  The existence of limitations when getting the results after applying clustering methods to the genes alone or to the samples alone has led to the development of new clustering methods which cluster the genes and the samples simultaneously. This method of clustering the genes and samples simultaneously, also called biclustering, co-clustering, or two-way clustering, searches for sub-matrices that exhibit high coherence. This thesis presents a unique biclustering schema that identifies highly coherent genes and conditions in microarray images. The technique of quadtree decomposition along with wavelet transformations analysis is carried out at various thresholds of multiresolution frequencies to retrieve decomposition of a microarray image. The resultant quadtrees at different thresholds are superimposed to identify the overlapping nodes. These nodes represent the potential biclusters of interest.
In this study biclusters with high coherency were retrieved. The biclusters were validated and compared with two of the well known methods in this field and significant number of genes was recovered in the found biclusters.

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