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This paper presents novel graph algorithms and modern control solutions applied to the graph networks resulting from specific experiments to discover disease-related pathways and drug targets in glioma cancer stem cells (GSCs). The theoretical framework applies to many other high-throughput data from experiments relevant to a variety of diseases. In addition to developing novel graph and control networks to predict therapeutic targets, these algorithms will provide biochemists with techniques to identify more metabolic regions and biological pathways for complex diseases, and design and test novel therapeutic solutions. |
