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Literature Mining for the Discovery of Hidden Connections between Drugs, Genes and Diseases

Overview of attention for article published in PLoS Computational Biology, September 2010
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257 Mendeley
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27 CiteULike
Title
Literature Mining for the Discovery of Hidden Connections between Drugs, Genes and Diseases
Published in
PLoS Computational Biology, September 2010
DOI 10.1371/journal.pcbi.1000943
Pubmed ID
Authors

Raoul Frijters, Marianne van Vugt, Ruben Smeets, René van Schaik, Jacob de Vlieg, Wynand Alkema

Abstract

The scientific literature represents a rich source for retrieval of knowledge on associations between biomedical concepts such as genes, diseases and cellular processes. A commonly used method to establish relationships between biomedical concepts from literature is co-occurrence. Apart from its use in knowledge retrieval, the co-occurrence method is also well-suited to discover new, hidden relationships between biomedical concepts following a simple ABC-principle, in which A and C have no direct relationship, but are connected via shared B-intermediates. In this paper we describe CoPub Discovery, a tool that mines the literature for new relationships between biomedical concepts. Statistical analysis using ROC curves showed that CoPub Discovery performed well over a wide range of settings and keyword thesauri. We subsequently used CoPub Discovery to search for new relationships between genes, drugs, pathways and diseases. Several of the newly found relationships were validated using independent literature sources. In addition, new predicted relationships between compounds and cell proliferation were validated and confirmed experimentally in an in vitro cell proliferation assay. The results show that CoPub Discovery is able to identify novel associations between genes, drugs, pathways and diseases that have a high probability of being biologically valid. This makes CoPub Discovery a useful tool to unravel the mechanisms behind disease, to find novel drug targets, or to find novel applications for existing drugs.

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 257 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 7 3%
Spain 4 2%
Netherlands 4 2%
United Kingdom 3 1%
India 3 1%
France 2 <1%
Germany 2 <1%
Hong Kong 1 <1%
Korea, Republic of 1 <1%
Other 7 3%
Unknown 223 87%

Demographic breakdown

Readers by professional status Count As %
Researcher 69 27%
Student > Ph. D. Student 67 26%
Student > Master 29 11%
Professor > Associate Professor 20 8%
Other 13 5%
Other 42 16%
Unknown 17 7%
Readers by discipline Count As %
Agricultural and Biological Sciences 74 29%
Computer Science 61 24%
Medicine and Dentistry 31 12%
Biochemistry, Genetics and Molecular Biology 14 5%
Chemistry 8 3%
Other 40 16%
Unknown 29 11%