Title |
The interactome: Predicting the protein-protein interactions in cells
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Published in |
Cellular & Molecular Biology Letters, October 2008
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DOI | 10.2478/s11658-008-0024-7 |
Pubmed ID | |
Authors |
Dariusz Plewczyński, Krzysztof Ginalski |
Abstract |
The term Interactome describes the set of all molecular interactions in cells, especially in the context of protein-protein interactions. These interactions are crucial for most cellular processes, so the full representation of the interaction repertoire is needed to understand the cell molecular machinery at the system biology level. In this short review, we compare various methods for predicting protein-protein interactions using sequence and structure information. The ultimate goal of those approaches is to present the complete methodology for the automatic selection of interaction partners using their amino acid sequences and/or three dimensional structures, if known. Apart from a description of each method, details of the software or web interface needed for high throughput prediction on the whole genome scale are also provided. The proposed validation of the theoretical methods using experimental data would be a better assessment of their accuracy. |
X Demographics
Geographical breakdown
Country | Count | As % |
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Israel | 1 | 100% |
Demographic breakdown
Type | Count | As % |
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Members of the public | 1 | 100% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
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United States | 3 | 3% |
Colombia | 2 | 2% |
Germany | 2 | 2% |
Brazil | 2 | 2% |
United Kingdom | 2 | 2% |
Norway | 1 | <1% |
South Africa | 1 | <1% |
Portugal | 1 | <1% |
India | 1 | <1% |
Other | 2 | 2% |
Unknown | 86 | 83% |
Demographic breakdown
Readers by professional status | Count | As % |
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Student > Ph. D. Student | 23 | 22% |
Researcher | 16 | 16% |
Student > Bachelor | 14 | 14% |
Student > Master | 13 | 13% |
Professor | 6 | 6% |
Other | 14 | 14% |
Unknown | 17 | 17% |
Readers by discipline | Count | As % |
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Agricultural and Biological Sciences | 40 | 39% |
Biochemistry, Genetics and Molecular Biology | 22 | 21% |
Computer Science | 6 | 6% |
Chemistry | 4 | 4% |
Engineering | 3 | 3% |
Other | 10 | 10% |
Unknown | 18 | 17% |