期刊
IEEE TRANSACTIONS ON NANOBIOSCIENCE
卷 14, 期 2, 页码 175-183出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TNB.2015.2391133
关键词
Gaussian interaction profile kernel; heterogeneous network; long non-coding RNAs; tissue expression
资金
- National Natural Science Foundation of China [61232001, 61428209, 61472133, 61420106009]
- Program for New Century Excellent Talents in University [NCET-12-0547]
With the knowledge of molecular biology it is highlighting that long non-coding RNAs (lncRNAs) play a critical role in many important biological processes, such as imprinting control, cell differentiation, immune responses, human diseases, tumorigenesis and other biological processes. This study proposes a novel computational method, named KRWRH, to infer disease-lincRNA associations with the influence of phenotype information and tissue expression details of lincRNA. Gaussian interaction profile kernel is calculated for diseases and lincRNAs and random walk with restart method is used for final prediction. The proposed method KRWRH is compared with four existing methods: LRLSLDA, TslncRNA, NRWRH, and RWRH. The experimental results based on the leave-one-out cross validation, ROC curves, and mean enrichment show that the proposed method KRWRH is able to predict known and unknown disease-lincRNA associations more effectively.
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