Early detection of a tumor's site of origin is particularly important for cancer diagnosis and treatment. The employment of gene expression profiles for different cancer types or subtypes has already shown significant advantages over traditional cancer classification methods. One of the major problems in cancer type recognition-oriented gene expression data analysis is the overwhelming number of measures of gene expression levels versus the small number of samples, which causes the curse of dimension issue. Here, we use diffusion maps, which interpret the eigenfunctions of Markov matrices as a system of coordinates on the original data set in order to obtain efficient representation of data geometric descriptions, for dimensionality reduction. The derived data are then clustered with Fuzzy ART to form the division of the cancer samples. Experimental results on the small round blue-cell tumor data set demonstrate the effectiveness of our proposed method in addressing multidimensional gene expression data and identifying different types of tumors.
R. Xu et al., "Applications of Diffusion Maps in Gene Expression Data-Based Cancer Diagnosis Analysis," Proceedings of the 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2007, Institute of Electrical and Electronics Engineers (IEEE), Aug 2007.
The definitive version is available at http://dx.doi.org/10.1109/IEMBS.2007.4353367
29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2007
Electrical and Computer Engineering
Mary K. Finley Missouri Endowment
National Science Foundation (U.S.)
Keywords and Phrases
Adaptive Resonance Theory; Markov Processes; Cancer; Cellular Biophysics; Eigenvalues and Eigenfunctions; Fuzzy Set Theory; Genetics; Learning (Artificial Intelligence); Medical Diagnostic Computing; Patient Diagnosis; Tumours
Article - Conference proceedings
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