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A genome-wide MeSH-based literature mining system predicts implicit gene-to-gene relationships and networks

Overview of attention for article published in BMC Systems Biology, October 2013
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Title
A genome-wide MeSH-based literature mining system predicts implicit gene-to-gene relationships and networks
Published in
BMC Systems Biology, October 2013
DOI 10.1186/1752-0509-7-s3-s9
Pubmed ID
Authors

Zuoshuang Xiang, Tingting Qin, Zhaohui S Qin, Yongqun He

Abstract

The large amount of literature in the post-genomics era enables the study of gene interactions and networks using all available articles published for a specific organism. MeSH is a controlled vocabulary of medical and scientific terms that is used by biomedical scientists to manually index articles in the PubMed literature database. We hypothesized that genome-wide gene-MeSH term associations from the PubMed literature database could be used to predict implicit gene-to-gene relationships and networks. While the gene-MeSH associations have been used to detect gene-gene interactions in some studies, different methods have not been well compared, and such a strategy has not been evaluated for a genome-wide literature analysis. Genome-wide literature mining of gene-to-gene interactions allows ranking of the best gene interactions and investigation of comprehensive biological networks at a genome level.

Mendeley readers

The data shown below were compiled from readership statistics for 29 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 29 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 8 28%
Researcher 7 24%
Student > Bachelor 2 7%
Professor 2 7%
Student > Master 2 7%
Other 4 14%
Unknown 4 14%
Readers by discipline Count As %
Agricultural and Biological Sciences 9 31%
Medicine and Dentistry 4 14%
Biochemistry, Genetics and Molecular Biology 4 14%
Computer Science 4 14%
Nursing and Health Professions 2 7%
Other 3 10%
Unknown 3 10%