Title |
Protein functional properties prediction in sparsely-label PPI networks through regularized non-negative matrix factorization
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Published in |
BMC Systems Biology, January 2015
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DOI | 10.1186/1752-0509-9-s1-s9 |
Pubmed ID | |
Authors |
Qingyao Wu, Zhenyu Wang, Chunshan Li, Yunming Ye, Yueping Li, Ning Sun |
Abstract |
Predicting functional properties of proteins in protein-protein interaction (PPI) networks presents a challenging problem and has important implication in computational biology. Collective classification (CC) that utilizes both attribute features and relational information to jointly classify related proteins in PPI networks has been shown to be a powerful computational method for this problem setting. Enabling CC usually increases accuracy when given a fully-labeled PPI network with a large amount of labeled data. However, such labels can be difficult to obtain in many real-world PPI networks in which there are usually only a limited number of labeled proteins and there are a large amount of unlabeled proteins. In this case, most of the unlabeled proteins may not connected to the labeled ones, the supervision knowledge cannot be obtained effectively from local network connections. As a consequence, learning a CC model in sparsely-labeled PPI networks can lead to poor performance. |
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Unknown | 1 | 100% |
Demographic breakdown
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Members of the public | 1 | 100% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
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United States | 1 | 4% |
Unknown | 23 | 96% |
Demographic breakdown
Readers by professional status | Count | As % |
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Student > Ph. D. Student | 4 | 17% |
Researcher | 4 | 17% |
Professor > Associate Professor | 3 | 13% |
Student > Master | 3 | 13% |
Student > Bachelor | 2 | 8% |
Other | 5 | 21% |
Unknown | 3 | 13% |
Readers by discipline | Count | As % |
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Computer Science | 11 | 46% |
Agricultural and Biological Sciences | 4 | 17% |
Engineering | 2 | 8% |
Biochemistry, Genetics and Molecular Biology | 1 | 4% |
Social Sciences | 1 | 4% |
Other | 1 | 4% |
Unknown | 4 | 17% |