Modules, networks and systems medicine for understanding disease and aiding diagnosis

Mika Gustafsson, Colm E. Nestor, Huan Zhang, Albert-Laszlo Barabasi, Sergio Baranzini, Søren Brunak, Kian Fan Chung, Howard J. Federoff, Anne-Claude Gavin, Richard R. Meehan, Paola Picotti, Miguel Angel Pujana, Nikolaus Rajewsky, Kenneth G. C. Smith, Peter J. Sterk, Pablo Villoslada, Mikael Benson

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    Abstract

    Many common diseases, such as asthma, diabetes or obesity, involve altered interactions between thousands of genes. High-throughput techniques (omics) allow identification of such genes and their products, but functional understanding is a formidable challenge. Network-based analyses of omics data have identified modules of disease-associated genes that have been used to obtain both a systems level and a molecular understanding of disease mechanisms. For example, in allergy a module was used to find a novel candidate gene that was validated by functional and clinical studies. Such analyses play important roles in systems medicine. This is an emerging discipline that aims to gain a translational understanding of the complex mechanisms underlying common diseases. In this review, we will explain and provide examples of how network-based analyses of omics data, in combination with functional and clinical studies, are aiding our understanding of disease, as well as helping to prioritize diagnostic markers or therapeutic candidate genes. Such analyses involve significant problems and limitations, which will be discussed. We also highlight the steps needed for clinical implementation.
    Original languageEnglish
    Article number82
    JournalGenome Medicine
    Volume6
    Issue number10
    Number of pages11
    ISSN1756-994X
    DOIs
    Publication statusPublished - 2014

    Bibliographical note

    © 2014 Gustafsson et al.; licensee BioMed Central Ltd. The licensee has exclusive rights to distribute this article, in any medium, for 12 months following its publication. After this time, the article is available under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver (http://creativecommons. org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

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