Title |
Fusing multiple protein-protein similarity networks to effectively predict lncRNA-protein interactions
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Published in |
BMC Bioinformatics, October 2017
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DOI | 10.1186/s12859-017-1819-1 |
Pubmed ID | |
Authors |
Xiaoxiong Zheng, Yang Wang, Kai Tian, Jiaogen Zhou, Jihong Guan, Libo Luo, Shuigeng Zhou |
Abstract |
Long non-coding RNA (lncRNA) plays important roles in many biological and pathological processes, including transcriptional regulation and gene regulation. As lncRNA interacts with multiple proteins, predicting lncRNA-protein interactions (lncRPIs) is an important way to study the functions of lncRNA. Up to now, there have been a few works that exploit protein-protein interactions (PPIs) to help the prediction of new lncRPIs. In this paper, we propose to boost the prediction of lncRPIs by fusing multiple protein-protein similarity networks (PPSNs). Concretely, we first construct four PPSNs based on protein sequences, protein domains, protein GO terms and the STRING database respectively, then build a more informative PPSN by fusing these four constructed PPSNs. Finally, we predict new lncRPIs by a random walk method with the fused PPSN and known lncRPIs. Our experimental results show that the new approach outperforms the existing methods. Fusing multiple protein-protein similarity networks can effectively boost the performance of predicting lncRPIs. |
X Demographics
Geographical breakdown
Country | Count | As % |
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United States | 1 | 33% |
France | 1 | 33% |
Unknown | 1 | 33% |
Demographic breakdown
Type | Count | As % |
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Scientists | 2 | 67% |
Members of the public | 1 | 33% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
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Unknown | 30 | 100% |
Demographic breakdown
Readers by professional status | Count | As % |
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Student > Ph. D. Student | 9 | 30% |
Researcher | 5 | 17% |
Student > Master | 3 | 10% |
Student > Bachelor | 2 | 7% |
Student > Doctoral Student | 1 | 3% |
Other | 4 | 13% |
Unknown | 6 | 20% |
Readers by discipline | Count | As % |
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Biochemistry, Genetics and Molecular Biology | 8 | 27% |
Agricultural and Biological Sciences | 7 | 23% |
Computer Science | 4 | 13% |
Environmental Science | 1 | 3% |
Mathematics | 1 | 3% |
Other | 3 | 10% |
Unknown | 6 | 20% |