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Nat Commun. 2014 Jun 26;5:4212. doi: 10.1038/ncomms5212.

Human symptoms-disease network.

Author information

1
1] School of Computer and Information Technology and Beijing Key Lab of Traffic Data Analysis and Mining, Beijing Jiaotong University, Beijing 100044, China [2] Center for Complex Network Research, Northeastern University Physics Department, 111 DA/Physics Dept., 110 Forsyth Street, Boston, Massachusetts 02115, USA [3] Center for Cancer Systems Biology, Dana-Farber Cancer Institute, Smith Bldg., Rm. 858A, 450 Brookline Ave, Boston, Massachusetts 02215, USA [4].
2
1] Center for Complex Network Research, Northeastern University Physics Department, 111 DA/Physics Dept., 110 Forsyth Street, Boston, Massachusetts 02115, USA [2] Center for Cancer Systems Biology, Dana-Farber Cancer Institute, Smith Bldg., Rm. 858A, 450 Brookline Ave, Boston, Massachusetts 02215, USA [3] Department of Theoretical Physics, Budapest University of Technology and Economics, Budafoki út. 8, 1111 Budapest, Hungary [4].
3
1] Center for Complex Network Research, Northeastern University Physics Department, 111 DA/Physics Dept., 110 Forsyth Street, Boston, Massachusetts 02115, USA [2] Center for Cancer Systems Biology, Dana-Farber Cancer Institute, Smith Bldg., Rm. 858A, 450 Brookline Ave, Boston, Massachusetts 02215, USA [3] Department of Theoretical Physics, Budapest University of Technology and Economics, Budafoki út. 8, 1111 Budapest, Hungary [4] Center for Network Science, Central European University, Nádor út. 9, 1051 Budapest, Hungary [5] Channing Division of Network Medicine, Brigham and Women's Hospital, Harvard Medical School, 181 Longwood Avenue, Boston, Massachusetts 02115, USA.
4
1] Center for Complex Network Research, Northeastern University Physics Department, 111 DA/Physics Dept., 110 Forsyth Street, Boston, Massachusetts 02115, USA [2] Center for Cancer Systems Biology, Dana-Farber Cancer Institute, Smith Bldg., Rm. 858A, 450 Brookline Ave, Boston, Massachusetts 02215, USA [3] Channing Division of Network Medicine, Brigham and Women's Hospital, Harvard Medical School, 181 Longwood Avenue, Boston, Massachusetts 02115, USA.

Abstract

In the post-genomic era, the elucidation of the relationship between the molecular origins of diseases and their resulting phenotypes is a crucial task for medical research. Here, we use a large-scale biomedical literature database to construct a symptom-based human disease network and investigate the connection between clinical manifestations of diseases and their underlying molecular interactions. We find that the symptom-based similarity of two diseases correlates strongly with the number of shared genetic associations and the extent to which their associated proteins interact. Moreover, the diversity of the clinical manifestations of a disease can be related to the connectivity patterns of the underlying protein interaction network. The comprehensive, high-quality map of disease-symptom relations can further be used as a resource helping to address important questions in the field of systems medicine, for example, the identification of unexpected associations between diseases, disease etiology research or drug design.

PMID:
24967666
DOI:
10.1038/ncomms5212
[Indexed for MEDLINE]

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