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Proceedings Paper

Visual exploratory analysis of integrated chromosome 19 proteomic data derived from glioma cancer stem-cell lines based on novel nonlinear dimensional data reduction techniques
Author(s): Sylvain Lespinats; Katja Pinker-Domenig; Uwe Meyer-Bäse; Anke Meyer-Bäse
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Paper Abstract

Chromosome 19 is known to be linked to neurodegeneration and many cancers. Glioma-derived cancer stem cells (GSCs) are tumor-initiating cells and may be refractory to radiation and chemotherapy and thus have important implications for tumor biology and therapeutics. The analysis and interpretation of large proteomic data sets requires the development of new data mining and visualization approaches. Traditional techniques are insufficient to interpret and visualize these resulting experimental data. The emphasis of this paper lies in the presentation of novel approaches for the visualization, clustering and projection representation to unveil hidden data structures relevant for the accurate interpretation of biological experiments. These qualitative and quantitative methods are applied to the proteomic analysis of data sets derived from the GSCs. The achieved clustering and visualization results provide a more detailed insight into the expression patterns for chromosome 19 proteins.

Paper Details

Date Published: 20 May 2015
PDF: 8 pages
Proc. SPIE 9496, Independent Component Analyses, Compressive Sampling, Large Data Analyses (LDA), Neural Networks, Biosystems, and Nanoengineering XIII, 949607 (20 May 2015); doi: 10.1117/12.2181814
Show Author Affiliations
Sylvain Lespinats, Institut National de l'Energie Solaire (France)
Katja Pinker-Domenig, Medizinische Univ. Wien (Austria)
Memorial Sloan-Kettering Cancer Ctr. (United States)
Uwe Meyer-Bäse, Florida State Univ. (United States)
Anke Meyer-Bäse, Florida State Univ. (United States)


Published in SPIE Proceedings Vol. 9496:
Independent Component Analyses, Compressive Sampling, Large Data Analyses (LDA), Neural Networks, Biosystems, and Nanoengineering XIII
Harold H. Szu; Liyi Dai; Yufeng Zheng, Editor(s)

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