人类症状-疾病网络。

Human symptoms-disease network.

机构信息

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].

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].

出版信息

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

DOI:10.1038/ncomms5212

PMID:24967666

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.

摘要

在后基因组时代，阐明疾病的分子起源与其表型之间的关系是医学研究的一项关键任务。在这里，我们使用大规模的生物医学文献数据库构建了一个基于症状的人类疾病网络，并研究了疾病的临床表现与潜在分子相互作用之间的联系。我们发现，两种疾病的基于症状的相似性与它们共享的遗传关联数量以及它们相关蛋白相互作用的程度密切相关。此外，疾病临床表现的多样性与潜在蛋白质相互作用网络的连接模式有关。全面、高质量的疾病-症状关系图可以进一步用作资源，帮助解决系统医学领域的重要问题，例如，疾病之间意外关联的识别、疾病病因研究或药物设计。