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学科主题: 医学信息学
题名:
Computational prediction of associations between long non-coding RNAs and proteins
作者: Lu, Qiongshi1,2,3; Ren, Sijin1,4; Lu, Ming1; Zhang, Yong5; Zhu, Dahai5; Zhang, Xuegong2,3; Li, Tingting1,4
关键词: Long non-coding RNA ; RNA-protein interaction ; Computation
刊名: BMC GENOMICS
发表日期: 2013-09-24
DOI: 10.1186/1471-2164-14-651
卷: 14, 期:0
收录类别: SCI
文章类型: Article
WOS标题词: Science & Technology
类目[WOS]: Biotechnology & Applied Microbiology ; Genetics & Heredity
研究领域[WOS]: Biotechnology & Applied Microbiology ; Genetics & Heredity
关键词[WOS]: AMINO-ACID-SEQUENCE ; DOSAGE COMPENSATION ; SECONDARY-STRUCTURE ; X-CHROMOSOME ; HUMAN-CELLS ; EVOLUTION ; DROSOPHILA ; GENE ; ROX1
英文摘要:

Background: Though most of the transcripts are long non-coding RNAs (lncRNAs), little is known about their functions. lncRNAs usually function through interactions with proteins, which implies the importance of identifying the binding proteins of lncRNAs in understanding the molecular mechanisms underlying the functions of lncRNAs. Only a few approaches are available for predicting interactions between lncRNAs and proteins. In this study, we introduce a new method lncPro.

Results: By encoding RNA and protein sequences into numeric vectors, we used matrix multiplication to score each RNA-protein pair. This score can be used to measure the interactions between an RNA-protein pair. This method effectively discriminates interacting and non-interacting RNA-protein pairs and predicts RNA-protein interactions within a given complex. Applying this method on all human proteins, we found that the long non-coding RNAs we collected tend to interact with nuclear proteins and RNA-binding proteins.

Conclusions: Compared with the existing approaches, our method shortens the time for training matrix and obtains optimal results based on the model being used. The ability of predicting the associations between lncRNAs and proteins has also been enhanced. Our method provides an idea on how to integrate different information into the prediction process.

语种: 英语
所属项目编号: 2011CBA01104 ; 2012CB316504 ; 2012AA020401 ; 31371337 ; 31030041
项目资助者: National Basic Research Program ; National High-tech R& ; D Program of China ; National Natural Science Foundation of China
WOS记录号: WOS:000326171900001
Citation statistics:
内容类型: 期刊论文
版本: 出版稿
URI标识: http://ir.bjmu.edu.cn/handle/400002259/65967
Appears in Collections:基础医学院_医学信息学系_期刊论文

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作者单位: 1.Peking Univ, Hlth Sci Ctr, Sch Basic Med Sci, Dept Biomed Informat, Beijing 100191, Peoples R China
2.Tsinghua Univ, TNLIST Dept Automat, MOE Key Lab Bioinformat, Beijing 100084, Peoples R China
3.Tsinghua Univ, TNLIST Dept Automat, Bioinformat Div, Beijing 100084, Peoples R China
4.Peking Univ, Sch Basic Med Sci, Hlth Sci Ctr, Inst Syst Biomed, Beijing 100191, Peoples R China
5.Chinese Acad Med Sci, Peking Union Med Coll, Sch Basic Med, Natl Lab Med Mol Biol,Inst Basic Med Sci, Beijing 100730, Peoples R China

Recommended Citation:
Lu, Qiongshi,Ren, Sijin,Lu, Ming,et al. Computational prediction of associations between long non-coding RNAs and proteins[J]. BMC GENOMICS,2013,14(0).
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