Visual Exploratory Analysis Of Integrated Chromosome 19 Proteomic Data Derived From Glioma Cancer Stem-Cell Lines Based On Novel Nonlinear Dimensional Data Reduction Techniques

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 paradigms. Traditional machine learning techniques proved to be insufficient to interpret and visualize the available experimental proteomic data. To overcome these limitations, we will present 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 of chromosome 19 proteins.