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Genome Med. 2014 Oct 17;6(10):82. doi: 10.1186/s13073-014-0082-6. eCollection 2014.

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

Author information

1
Centre for Individualized Medicine, Department of Pediatrics, Faculty of Medicine, 58185 Linköping, Sweden.
2
Department of Physics, Biology and Computer Science, Center for Complex Network Research, Northeastern University, Boston, MA 02115 USA.
3
Department of Neurology, University of California, San Francisco, CA 94143 USA.
4
Center for Biological Sequence Analysis, Department of Systems Biology, Technical University of Denmark, DK-2800 Lyngby, Denmark ; Novo Nordisk Foundation Center for Protein Research, Faculty of Health Sciences, University of Copenhagen, DK-2200 Copenhagen, Denmark.
5
Airways Disease Section, National Heart and Lung Institute, Imperial College London, London, SW3 6LY UK.
6
Department of Neurology and Neuroscience, Georgetown University Medical Center, Washington, DC 20057 USA.
7
European Molecular Biology Laboratory, 69117 Heidelberg, Germany.
8
MRC Human Genetics Unit, MRC IGMM, University of Edinburgh, Edinburgh, EH4 2XU UK.
9
Institute of Biochemistry, University of Zürich, 8093 Zürich, Switzerland.
10
Catalan Institute of Oncology, Bellvitge Biomedical Research Institute (IDIBELL), Barcelona, 08908 Spain.
11
Systems Biology of Gene Regulatory Elements, Max-Delbrück-Center for Molecular Medicine, Robert-Rössle-Strasse 10, 13125 Berlin, Germany.
12
Cambridge Institute for Medical Research, University of Cambridge, Cambridge Biomedical Campus, Cambridge, CB2 0XY UK ; Department of Medicine, University of Cambridge School of Clinical Medicine, Addenbrooke's Hospital, Cambridge, CB2 0QQ UK.
13
Department of Respiratory Medicine, Academic Medical Centre, University of Amsterdam, 1100 DE Amsterdam, The Netherlands.
14
Center of Neuroimmunology and Department of Neurology, Institut d'investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Hospital Clinic of Barcelona, 08028 Barcelona, Spain.

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.

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