Title |
Enhancing the Prioritization of Disease-Causing Genes through Tissue Specific Protein Interaction Networks
|
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Published in |
PLoS Computational Biology, September 2012
|
DOI | 10.1371/journal.pcbi.1002690 |
Pubmed ID | |
Authors |
Oded Magger, Yedael Y. Waldman, Eytan Ruppin, Roded Sharan |
Abstract |
The prioritization of candidate disease-causing genes is a fundamental challenge in the post-genomic era. Current state of the art methods exploit a protein-protein interaction (PPI) network for this task. They are based on the observation that genes causing phenotypically-similar diseases tend to lie close to one another in a PPI network. However, to date, these methods have used a static picture of human PPIs, while diseases impact specific tissues in which the PPI networks may be dramatically different. Here, for the first time, we perform a large-scale assessment of the contribution of tissue-specific information to gene prioritization. By integrating tissue-specific gene expression data with PPI information, we construct tissue-specific PPI networks for 60 tissues and investigate their prioritization power. We find that tissue-specific PPI networks considerably improve the prioritization results compared to those obtained using a generic PPI network. Furthermore, they allow predicting novel disease-tissue associations, pointing to sub-clinical tissue effects that may escape early detection. |
X Demographics
Geographical breakdown
Country | Count | As % |
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United Kingdom | 1 | 50% |
Japan | 1 | 50% |
Demographic breakdown
Type | Count | As % |
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Scientists | 2 | 100% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
United States | 8 | 4% |
Japan | 3 | 2% |
Germany | 2 | 1% |
India | 2 | 1% |
Brazil | 1 | <1% |
Sweden | 1 | <1% |
United Kingdom | 1 | <1% |
Italy | 1 | <1% |
France | 1 | <1% |
Other | 1 | <1% |
Unknown | 162 | 89% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Ph. D. Student | 68 | 37% |
Researcher | 39 | 21% |
Student > Master | 23 | 13% |
Student > Bachelor | 11 | 6% |
Professor > Associate Professor | 7 | 4% |
Other | 21 | 11% |
Unknown | 14 | 8% |
Readers by discipline | Count | As % |
---|---|---|
Agricultural and Biological Sciences | 73 | 40% |
Biochemistry, Genetics and Molecular Biology | 32 | 17% |
Computer Science | 29 | 16% |
Engineering | 8 | 4% |
Medicine and Dentistry | 6 | 3% |
Other | 12 | 7% |
Unknown | 23 | 13% |