中国科学院机构知识库网格
Chinese Academy of Sciences Institutional Repositories Grid
LLCLPLDA: a novel model for predicting lncRNA-disease associations

文献类型:期刊论文

AuthorXie, Guobo2; Huang, Shuhuang2; Luo, Yu2; Ma, Lei1; Lin, Zhiyi2; Sun, Yuping2
SourceMOLECULAR GENETICS AND GENOMICS
Issued Date2019-12-01
Volume294Issue:6Pages:1477-1486
ISSN1617-4615
KeywordLocality-constrained linear coding Label propagation lncRNA-disease associations Prediction
DOI10.1007/s00438-019-01590-8
Corresponding AuthorLuo, Yu(yuluo@gdut.edu.cn) ; Ma, Lei(lei.ma@ia.ac.cn)
English AbstractLong noncoding RNAs play a significant role in the occurrence of diseases. Thus, studying the relationship prediction between lncRNAs and disease is becoming more popular. Researchers hope to determine effective treatments by revealing the occurrence and development of diseases at the molecular level. However, the traditional biological experimental way to verify the association between lncRNAs and disease is very time-consuming and expensive. Therefore, we developed a method called LLCLPLDA to predict potential lncRNA-disease associations. First, locality-constrained linear coding (LLC) is leveraged to project the features of lncRNAs and diseases to local-constraint features, and then, a label propagation (LP) strategy is used to mix up the initial association matrix and the obtained features of lncRNAs and diseases. To demonstrate the performance of our method, we compared LLCLPLDA with five methods in the leave-one-out cross-validation and fivefold cross-validation scheme, and the experimental results show that the proposed method outperforms the other five methods. Additionally, we conducted case studies on three diseases: cervical cancer, gliomas, and breast cancer. The top five predicted lncRNAs for cervical cancer and gliomas were verified, and four of the five lncRNAs for breast cancer were also confirmed.
WOS KeywordLARGE NONCODING RNAS ; CERVICAL-CANCER ; HUMAN GLIOMA ; BREAST ; GENOME ; IDENTIFICATION ; EXPRESSION ; INSIGHTS ; CELLS
Funding ProjectNational Natural Science Foundation of China[618002072] ; National Natural Science Foundation of China[61702112] ; Natural Science Foundation of Guangdong Province[2018A030313389] ; Science and Technology Plan Project of Guangdong Province[2017A040405050] ; Science and Technology Plan Project of Guangdong Province[2016B030306004] ; Science and Technology Plan Project of Guangdong Province[2016B030301008] ; Opening Project of the Guangdong Province Key Laboratory of Computational Science[2018012]
WOS Research AreaBiochemistry & Molecular Biology ; Genetics & Heredity
Language英语
WOS IDWOS:000494505700008
PublisherSPRINGER HEIDELBERG
Funding OrganizationNational Natural Science Foundation of China ; Natural Science Foundation of Guangdong Province ; Science and Technology Plan Project of Guangdong Province ; Opening Project of the Guangdong Province Key Laboratory of Computational Science
源URL[http://ir.ia.ac.cn/handle/173211/28886]  
Collection中国科学院自动化研究所
Corresponding AuthorLuo, Yu; Ma, Lei
Affiliation1.Chinese Acad Sci, Inst Automat, Beijing 100190, Peoples R China
2.Guangdong Univ Technol, Sch Comp Sci, Guangzhou, Guangdong, Peoples R China
Recommended Citation
GB/T 7714
Xie, Guobo,Huang, Shuhuang,Luo, Yu,et al. LLCLPLDA: a novel model for predicting lncRNA-disease associations[J]. MOLECULAR GENETICS AND GENOMICS,2019,294(6):1477-1486.
APA Xie, Guobo,Huang, Shuhuang,Luo, Yu,Ma, Lei,Lin, Zhiyi,&Sun, Yuping.(2019).LLCLPLDA: a novel model for predicting lncRNA-disease associations.MOLECULAR GENETICS AND GENOMICS,294(6),1477-1486.
MLA Xie, Guobo,et al."LLCLPLDA: a novel model for predicting lncRNA-disease associations".MOLECULAR GENETICS AND GENOMICS 294.6(2019):1477-1486.

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来源:自动化研究所

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