Title |
Predicting protein-protein interactions in unbalanced data using the primary structure of proteins
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Published in |
BMC Bioinformatics, April 2010
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DOI | 10.1186/1471-2105-11-167 |
Pubmed ID | |
Authors |
Chi-Yuan Yu, Lih-Ching Chou, Darby Tien-Hao Chang |
Abstract |
Elucidating protein-protein interactions (PPIs) is essential to constructing protein interaction networks and facilitating our understanding of the general principles of biological systems. Previous studies have revealed that interacting protein pairs can be predicted by their primary structure. Most of these approaches have achieved satisfactory performance on datasets comprising equal number of interacting and non-interacting protein pairs. However, this ratio is highly unbalanced in nature, and these techniques have not been comprehensively evaluated with respect to the effect of the large number of non-interacting pairs in realistic datasets. Moreover, since highly unbalanced distributions usually lead to large datasets, more efficient predictors are desired when handling such challenging tasks. |
X Demographics
Geographical breakdown
Country | Count | As % |
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Japan | 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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Germany | 3 | 3% |
Canada | 3 | 3% |
Japan | 2 | 2% |
Brazil | 2 | 2% |
India | 1 | <1% |
Ecuador | 1 | <1% |
Colombia | 1 | <1% |
Belgium | 1 | <1% |
Iran, Islamic Republic of | 1 | <1% |
Other | 2 | 2% |
Unknown | 87 | 84% |
Demographic breakdown
Readers by professional status | Count | As % |
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Student > Ph. D. Student | 29 | 28% |
Researcher | 23 | 22% |
Student > Master | 12 | 12% |
Student > Bachelor | 7 | 7% |
Professor > Associate Professor | 6 | 6% |
Other | 11 | 11% |
Unknown | 16 | 15% |
Readers by discipline | Count | As % |
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Agricultural and Biological Sciences | 44 | 42% |
Computer Science | 23 | 22% |
Biochemistry, Genetics and Molecular Biology | 11 | 11% |
Medicine and Dentistry | 2 | 2% |
Engineering | 2 | 2% |
Other | 6 | 6% |
Unknown | 16 | 15% |