Title |
Network-based analysis reveals distinct association patterns in a semantic MEDLINE-based drug-disease-gene network
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Published in |
Journal of Biomedical Semantics, August 2014
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DOI | 10.1186/2041-1480-5-33 |
Pubmed ID | |
Authors |
Yuji Zhang, Cui Tao, Guoqian Jiang, Asha A Nair, Jian Su, Christopher G Chute, Hongfang Liu |
Abstract |
A huge amount of associations among different biological entities (e.g., disease, drug, and gene) are scattered in millions of biomedical articles. Systematic analysis of such heterogeneous data can infer novel associations among different biological entities in the context of personalized medicine and translational research. Recently, network-based computational approaches have gained popularity in investigating such heterogeneous data, proposing novel therapeutic targets and deciphering disease mechanisms. However, little effort has been devoted to investigating associations among drugs, diseases, and genes in an integrative manner. |
X Demographics
Geographical breakdown
Country | Count | As % |
---|---|---|
Austria | 1 | 20% |
France | 1 | 20% |
United States | 1 | 20% |
Unknown | 2 | 40% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Scientists | 3 | 60% |
Members of the public | 2 | 40% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
Spain | 2 | 4% |
Japan | 1 | 2% |
Netherlands | 1 | 2% |
Brazil | 1 | 2% |
Unknown | 46 | 90% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Master | 12 | 24% |
Student > Ph. D. Student | 12 | 24% |
Researcher | 8 | 16% |
Student > Doctoral Student | 4 | 8% |
Professor | 3 | 6% |
Other | 3 | 6% |
Unknown | 9 | 18% |
Readers by discipline | Count | As % |
---|---|---|
Computer Science | 14 | 27% |
Biochemistry, Genetics and Molecular Biology | 8 | 16% |
Agricultural and Biological Sciences | 6 | 12% |
Medicine and Dentistry | 6 | 12% |
Nursing and Health Professions | 1 | 2% |
Other | 5 | 10% |
Unknown | 11 | 22% |