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Human symptoms–disease network

Author

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  • XueZhong Zhou

    (School of Computer and Information Technology and Beijing Key Lab of Traffic Data Analysis and Mining, Beijing Jiaotong University
    Center for Complex Network Research, 111 DA/Physics Dept.
    Center for Cancer Systems Biology, Dana-Farber Cancer Institute)

  • Jörg Menche

    (Center for Complex Network Research, 111 DA/Physics Dept.
    Center for Cancer Systems Biology, Dana-Farber Cancer Institute
    Budapest University of Technology and Economics)

  • Albert-László Barabási

    (Center for Complex Network Research, 111 DA/Physics Dept.
    Center for Cancer Systems Biology, Dana-Farber Cancer Institute
    Budapest University of Technology and Economics
    Center for Network Science, Central European University)

  • Amitabh Sharma

    (Center for Complex Network Research, 111 DA/Physics Dept.
    Center for Cancer Systems Biology, Dana-Farber Cancer Institute
    Brigham and Women’s Hospital, Harvard Medical School)

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.

Suggested Citation

  • XueZhong Zhou & Jörg Menche & Albert-László Barabási & Amitabh Sharma, 2014. "Human symptoms–disease network," Nature Communications, Nature, vol. 5(1), pages 1-10, September.
    • Handle: RePEc:nat:natcom:v:5:y:2014:i:1:d:10.1038_ncomms5212
    DOI: 10.1038/ncomms5212
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    Cited by:

    1. Pisanu Buphamalai & Tomislav Kokotovic & Vanja Nagy & Jörg Menche, 2021. "Network analysis reveals rare disease signatures across multiple levels of biological organization," Nature Communications, Nature, vol. 12(1), pages 1-15, December.
    2. Bähren, Tobias & Braka, Daniel & Burchard, Patrick & Cyron, Stanley & Demary, Marius & Dragieva, Martina & Eis, Luke & Farid, Abdul Tanwir & Gomes, Daniel & Hacker, Milan & Kaiser, Julian & Krüger, Ro, 2021. "Der Einsatz von grünem Tee und anderen Polyphenolen in der Medizin ‒ eine Big-Data-Analyse der medizinischen Fachliteratur," ifid Schriftenreihe: Beiträge zu IT-Management & Digitalisierung, FOM Hochschule für Oekonomie & Management, ifid Institut für IT-Management & Digitalisierung, volume 1, number 1 edited by FOM Hochschule für Oekonomie & Management, Institut für IT-Management & Digitalisierung (ifid), July.
    3. Shin, Hyunjin & Woo, Hyun Goo & Sohn, Kyung-Ah & Lee, Sungjoo, 2023. "Comparing research trends with patenting activities in the biomedical sector: The case of dementia," Technological Forecasting and Social Change, Elsevier, vol. 195(C).
    4. Jiang, Xiong-Fei & Xiong, Long & Bai, Ling & Lin, Jie & Zhang, Jing-Feng & Yan, Kun & Zhu, Jia-Zhen & Zheng, Bo & Zheng, Jian-Jun, 2022. "Structure and dynamics of human complication-disease network," Chaos, Solitons & Fractals, Elsevier, vol. 164(C).
    5. Nastaran Allahyari & Amir Kargaran & Ali Hosseiny & G R Jafari, 2022. "The structure balance of gene-gene networks beyond pairwise interactions," PLOS ONE, Public Library of Science, vol. 17(3), pages 1-18, March.

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