Mapping molecular association networks of nervous system diseases via large-scale analysis of published research

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  • Network medicine has been applied successfully to elicit the structure of large-scale molecular interaction networks. Its main proponents have claimed that this approach to integrative medical investigation should make it possible to identify functional modules of interacting molecular biological units as well as interactions themselves. This paper takes a significant step in this direction. Based on a large-scale analysis of the nervous system molecular medicine literature, this study analyzes and visualizes the complex structure of associations between diseases on the one hand and all types of molecular substances on the other. From this analysis it then identifies functional co-association groups consisting of several types of molecular substances, each consisting of substances that exhibit a pattern of frequent co-association with similar diseases. These groups in turn exhibit interlinking in a complex pattern, suggesting that such complex interactions between functional molecular modules may play a role in disease etiology. We find that the patterns exhibited by the networks of disease – molecular substance associations studied here correspond well to a number of recently published research results, and that the groups of molecular substances identified by statistical analysis of these networks do appear to be interesting groups of molecular substances that are interconnected in identifiable and interpretable ways. Our results not only demonstrate that networks are a convenient framework to analyze and visualize large-scale, complex relationships among molecular networks and diseases, but may also provide a conceptual basis for bridging gaps in experimental and theoretical knowledge.

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  • Type of Item
    Article (Published)
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    Attribution 4.0 International
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  • Citation for previous publication
    • Hu, X.,  Zhao, D., and Strotmann, A. (2013). Mapping molecular association networks of nervous system diseases via large-scale analysis of published research. PLOS ONE, 8(6), .
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