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论文题目: Global Prioritizing Disease Candidate lncRNAs via a Multi-level Composite Network
英文论文题目: Global Prioritizing Disease Candidate lncRNAs via a Multi-level Composite Network
第一作者: Yao, QL; Wu, LL; Li, J; Yang, LG; Sun, YD; Li, Z; He, S; Feng, FYM; Li, H; Li, YX
英文第一作者: Yao, QL; Wu, LL; Li, J; Yang, LG; Sun, YD; Li, Z; He, S; Feng, FYM; Li, H; Li, YX
联系作者: Li, H; Li, YX (reprint author), Chinese Acad Sci, Shanghai Inst Biol Sci, CAS MPG Partner Inst Computat Biol, CAS Key Lab Computat Biol, Shanghai 200031, Peoples R China.
英文联系作者: Li, H; Li, YX (reprint author), Chinese Acad Sci, Shanghai Inst Biol Sci, CAS MPG Partner Inst Computat Biol, CAS Key Lab Computat Biol, Shanghai 200031, Peoples R China.
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发表年度: 2017
卷: 7
期:
页码: 39516
摘要: LncRNAs play pivotal roles in many important biological processes, but research on the functions of lncRNAs in human disease is still in its infancy. Therefore, it is urgent to prioritize lncRNAs that are potentially associated with diseases. In this work, we developed a novel algorithm, LncPriCNet, that uses a multi-level composite network to prioritize candidate lncRNAs associated with diseases. By integrating genes, lncRNAs, phenotypes and their associations, LncPriCNet achieves an overall performance superior to that of previous methods, with high AUC values of up to 0.93. Notably, LncPriCNet still performs well when information on known disease lncRNAs is lacking. When applied to breast cancer, LncPriCNet identified known breast cancer-related lncRNAs, revealed novel lncRNA candidates and inferred their functions via pathway analysis. We further constructed the human disease-lncRNA landscape, revealed the modularity of the disease-lncRNA network and identified several lncRNA hotspots. In summary, LncPriCNet is a useful tool for prioritizing disease-related lncRNAs and may facilitate understanding of the molecular mechanisms of human disease at the lncRNA level.
英文摘要: LncRNAs play pivotal roles in many important biological processes, but research on the functions of lncRNAs in human disease is still in its infancy. Therefore, it is urgent to prioritize lncRNAs that are potentially associated with diseases. In this work, we developed a novel algorithm, LncPriCNet, that uses a multi-level composite network to prioritize candidate lncRNAs associated with diseases. By integrating genes, lncRNAs, phenotypes and their associations, LncPriCNet achieves an overall performance superior to that of previous methods, with high AUC values of up to 0.93. Notably, LncPriCNet still performs well when information on known disease lncRNAs is lacking. When applied to breast cancer, LncPriCNet identified known breast cancer-related lncRNAs, revealed novel lncRNA candidates and inferred their functions via pathway analysis. We further constructed the human disease-lncRNA landscape, revealed the modularity of the disease-lncRNA network and identified several lncRNA hotspots. In summary, LncPriCNet is a useful tool for prioritizing disease-related lncRNAs and may facilitate understanding of the molecular mechanisms of human disease at the lncRNA level.
刊物名称: SCIENTIFIC REPORTS
英文刊物名称: SCIENTIFIC REPORTS
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学科: Multidisciplinary Sciences
英文学科: Multidisciplinary Sciences
影响因子: 4.259
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论文类别: Article
英文论文类别: Article
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2014 中国科学院上海生命科学研究院 版权所有