Abstract
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.
Highlights
Information science can help to identify interesting approaches and provide new perspectives for scientific research [1,2]
The results provide a series of visualizations of networks of molecular substances associated with nervous system disease genetics, derived and integrated from a large collection of published research (Note that this approach is very different from the traditional meta-analysis one, as the methodology to obtain and visualize these networks explains in the Materials and Method section)
Alzheimer disease is the largest disease node in the network, brain neoplasms are second, and Parkinson’s disease is third, indicating that they are most extensively associated to molecular substances in their respective subnetworks, which suggests that they are the three most complex of the system diseases in this network
Summary
Information science can help to identify interesting approaches and provide new perspectives for scientific research [1,2]. The concept of network medicine is gaining attention in biomedical research and providing a new promising approach to discovering targets for the treatment of diseases [3,4,5,6,7,8]. Advocates of the network medicine approach foresee in particular its potential to provide an improved view of the whole system of the human body, its diseases and their contributing factors, and to help bridge the gap between molecular biology and clinical medicine [3]. One striking example of this is research that was undertaken to replicate published associations between 85 DNA variants and acute coronary syndromes. Of the 85 variants tested, only 1 showed a rise to a nominally significant P value, highlighting a complete lack of support for the validity of hypothesis that any of the variants previously reported in scores of publications are associated with acute coronary syndromes [9,11]
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