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Open AccessArticle

A Probabilistic Matrix Factorization Method for Identifying lncRNA-Disease Associations

by Zhanwei Xuan 1,2, Jiechen Li 1,2, Jingwen Yu 1,2, Xiang Feng 1,2, Bihai Zhao 1 and Lei Wang 1,2,*
1
College of Computer Engineering & Applied Mathematics, Changsha University, Changsha 410001, China
2
Key Laboratory of Hunan Province for Internet of Things and Information Security, Xiangtan University, Xiangtan 411105, China
*
Author to whom correspondence should be addressed.
Genes 2019, 10(2), 126; https://doi.org/10.3390/genes10020126
Received: 29 December 2018 / Revised: 31 January 2019 / Accepted: 4 February 2019 / Published: 8 February 2019
(This article belongs to the Section Technologies and Resources for Genetics)
Recently, an increasing number of studies have indicated that long-non-coding RNAs (lncRNAs) can participate in various crucial biological processes and can also be used as the most promising biomarkers for the treatment of certain diseases such as coronary artery disease and various cancers. Due to costs and time complexity, the number of possible disease-related lncRNAs that can be verified by traditional biological experiments is very limited. Therefore, in recent years, it has been very popular to use computational models to predict potential disease-lncRNA associations. In this study, we constructed three kinds of association networks, namely the lncRNA-miRNA association network, the miRNA-disease association network, and the lncRNA-disease correlation network firstly. Then, through integrating these three newly constructed association networks, we constructed an lncRNA-disease weighted association network, which would be further updated by adopting the KNN algorithm based on the semantic similarity of diseases and the similarity of lncRNA functions. Thereafter, according to the updated lncRNA-disease weighted association network, a novel computational model called PMFILDA was proposed to infer potential lncRNA-disease associations based on the probability matrix decomposition. Finally, to evaluate the superiority of the new prediction model PMFILDA, we performed Leave One Out Cross-Validation (LOOCV) based on strongly validated data filtered from MNDR and the simulation results indicated that the performance of PMFILDA was better than some state-of-the-art methods. Moreover, case studies of breast cancer, lung cancer, and colorectal cancer were implemented to further estimate the performance of PMFILDA, and simulation results illustrated that PMFILDA could achieve satisfying prediction performance as well. View Full-Text
Keywords: lncRNA; disease; miRNA; lncRNA-disease associations; identifying disease-related lncRNA lncRNA; disease; miRNA; lncRNA-disease associations; identifying disease-related lncRNA
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MDPI and ACS Style

Xuan, Z.; Li, J.; Yu, J.; Feng, X.; Zhao, B.; Wang, L. A Probabilistic Matrix Factorization Method for Identifying lncRNA-Disease Associations. Genes 2019, 10, 126.

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