Dr. Sumeet Dua

Max P. & Robbie L. Watson Eminent Scholar Chair

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Kaustubh Sabnis (2004)

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Computational Identification of Tumor Gene Markers Using Novel Dimensionality Reduction and Unsupervised Classification Techniques; MS-CS   Thesis; Student: Kaustubh Sabnis (2004)

The successful treatment of cancer depends on how early and how accurately it is detected. Molecular diagnosis has great potential to predict the diagnosis precisely as compared to clinical diagnosis. Biologists do not have complete information about the molecular markers that are responsible for causing most solid tumors. Because of the massive amount of genes present in homo sapiens, it is practically impossible to find the genes responsible for each type of cancer class by carrying out experiments in a wet laboratory. We propose to find highly informative genes, responsible for multiple cancer types using discrete wavelet transformations as a dimensionality reduction technique.
Discrete wavelet transformations are applied to a preprocessed GCM cancer gene expression data giving orthonormal wavelet coefficients for each sample. These coefficients are passed through two filters: one filter selects coefficients with top energy levels, and the second filter chooses a common set of coefficients among samples belonging to the same cancer class. Inverse DWT is applied to these filtered coefficients yielding marker genes per cancer class. We cross-validate these results against a biologically significant database of cancer genes. A total of 21 genes spanning 7 cancer classes are found in common with the cancer gene database. With the exception of two cancer classes (breast and bladder), we identify 41.67 % more cancer causing genes than the method used by Ramaswamy et al.

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