中国科学院机构知识库网格
Chinese Academy of Sciences Institutional Repositories Grid
首页 机构 成果 学者
  
  1. CAS IR Grid
  2. 自动化研究所
  3. 中国科学院自动化研究所
  4. 复杂系统认知与决策实验室
  5. 类脑芯片与系统研究
LLCLPLDA: a novel model for predicting lncRNA-disease associations

文献类型：期刊论文

作者Xie, Guobo2; Huang, Shuhuang2; Luo, Yu2; Ma, Lei1; Lin, Zhiyi2; Sun, Yuping2
刊名MOLECULAR GENETICS AND GENOMICS
出版日期2019-12-01
卷号294期号:6页码:1477-1486
ISSN号1617-4615
关键词Locality-constrained linear coding Label propagation lncRNA-disease associations Prediction
DOI10.1007/s00438-019-01590-8
通讯作者Luo, Yu(yuluo@gdut.edu.cn) ; Ma, Lei(lei.ma@ia.ac.cn)
英文摘要Long 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关键词LARGE NONCODING RNAS ; CERVICAL-CANCER ; HUMAN GLIOMA ; BREAST ; GENOME ; IDENTIFICATION ; EXPRESSION ; INSIGHTS ; CELLS
资助项目National 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研究方向Biochemistry & Molecular Biology ; Genetics & Heredity
语种英语
出版者SPRINGER HEIDELBERG
WOS记录号WOS:000494505700008
资助机构National 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]  
专题类脑芯片与系统研究
通讯作者Luo, Yu; Ma, Lei
作者单位1.Chinese Acad Sci, Inst Automat, Beijing 100190, Peoples R China
2.Guangdong Univ Technol, Sch Comp Sci, Guangzhou, Guangdong, Peoples R China
推荐引用方式
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.

入库方式： OAI收割

来源：自动化研究所

浏览0
下载0
收藏0
其他版本

除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。