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a pubmed-wide associational study of infectious diseases一个pubmed-wide联想的传染性疾病的研究
A PubMed-Wide Associational Study of Infectious
Diseases
1,2,3 1 1 1 1
Vitali Sintchenko *, Stephen Anthony , Xuan-Hieu Phan , Frank Lin , Enrico W. Coiera
1 Centre for Health Informatics, University of New South Wales, Sydney, New South Wales, Australia, 2 Centre for Infectious Diseases and Microbiology, Sydney Medical
School, The University of Sydney, Sydney, New South Wales, Australia, 3 Institute of Clinical Pathology and Medical Research, Westmead Hospital, Sydney West Area
Health Service, Sydney, New South Wales, Australia
Abstract
Background: Computational discovery is playing an ever-greater role in supporting the processes of knowledge synthesis. A
significant proportion of the more than 18 million manuscripts indexed in the PubMed database describe infectious disease
syndromes and various infectious agents. This study is the first attempt to integrate online repositories of text-based
publications and microbial genome databases in order to explore the dynamics of relationships between pathogens and
infectious diseases.
Methodology/Principal Findings: Herein we demonstrate how the knowledge space of infectious diseases can be
computationally represented and quantified, and tracked over time. The knowledge space is explored by mapping of the
infectious disease literature, looking at dynamics of literature deposition, zooming in from pathogen to genome level and
searching for new associations. Syndromic signatures for different pathogens can be created to enable a new and clinically
focussed reclassification of the microbial world. Examples of syndrome and pathogen networks illustrate how multilevel
network representations of the relationships bet
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