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Proteomic Data Analysis of Glioma Cancer Stem-Cell Lines Based on Novel Nonlinear Dimensional Data Reduction Techniques

机译:基于新型非线性降维技术的胶质瘤癌干细胞系蛋白质组学数据分析

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摘要

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 application 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 protein-level fold changes and putative upstream regulators for the GSCs. However the extracted molecular information is insufficient in classifying GSCs and paving the pathway to an improved therapeutics of the heterogeneous glioma.
机译:胶质瘤来源的癌症干细胞(GSC)是肿瘤起源的细胞,可能对放射线和化学疗法无能为力,因此对肿瘤生物学和治疗学具有重要意义。大型蛋白质组数据集的分析和解释要求开发新的数据挖掘和可视化方法。传统技术不足以解释和可视化这些结果实验数据。本文的重点在于应用新颖的方法进行可视化,聚类和投影表示,以揭示与准确解释生物实验有关的隐藏数据结构。这些定性和定量方法适用于对来自GSC的数据集的蛋白质组学分析。获得的聚类和可视化结果为蛋白质水平的折叠变化和假定的GSC上游调节剂提供了更详细的信息。但是,所提取的分子信息不足以对GSC进行分类,并为改善异种神经胶质瘤的治疗方法铺平了道路。

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