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Article

A Novel Probability Model for LncRNA–Disease Association Prediction Based on the Naïve Bayesian Classifier

1
Key Laboratory of Intelligent Computing & Information Processing, Xiangtan University, Xiangtan 411105, China
2
College of Computer Engineering & Applied Mathematics, Changsha University, Changsha 410001, China
3
Department of Computer Science, Princeton University, Princeton, NJ 08544, USA
*
Author to whom correspondence should be addressed.
Genes 2018, 9(7), 345; https://doi.org/10.3390/genes9070345
Received: 26 May 2018 / Revised: 24 June 2018 / Accepted: 3 July 2018 / Published: 8 July 2018
An increasing number of studies have indicated that long-non-coding RNAs (lncRNAs) play crucial roles in biological processes, complex disease diagnoses, prognoses, and treatments. However, experimentally validated associations between lncRNAs and diseases are still very limited. Recently, computational models have been developed to discover potential associations between lncRNAs and diseases by integrating multiple heterogeneous biological data; this has become a hot topic in biological research. In this article, we constructed a global tripartite network by integrating a variety of biological information including miRNA–disease, miRNA–lncRNA, and lncRNA–disease associations and interactions. Then, we constructed a global quadruple network by appending gene–lncRNA interaction, gene–disease association, and gene–miRNA interaction networks to the global tripartite network. Subsequently, based on these two global networks, a novel approach was proposed based on the naïve Bayesian classifier to predict potential lncRNA–disease associations (NBCLDA). Comparing with the state-of-the-art methods, our new method does not entirely rely on known lncRNA–disease associations, and can achieve a reliable performance with effective area under ROC curve (AUCs)in leave-one-out cross validation. Moreover, in order to further estimate the performance of NBCLDA, case studies of colorectal cancer, prostate cancer, and glioma were implemented in this paper, and the simulation results demonstrated that NBCLDA can be an excellent tool for biomedical research in the future. View Full-Text
Keywords: lncRNA–disease associations; tripartite network; quadruple network; prediction model; Naïve Bayesian Classifier lncRNA–disease associations; tripartite network; quadruple network; prediction model; Naïve Bayesian Classifier
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MDPI and ACS Style

Yu, J.; Ping, P.; Wang, L.; Kuang, L.; Li, X.; Wu, Z. A Novel Probability Model for LncRNA–Disease Association Prediction Based on the Naïve Bayesian Classifier. Genes 2018, 9, 345. https://doi.org/10.3390/genes9070345

AMA Style

Yu J, Ping P, Wang L, Kuang L, Li X, Wu Z. A Novel Probability Model for LncRNA–Disease Association Prediction Based on the Naïve Bayesian Classifier. Genes. 2018; 9(7):345. https://doi.org/10.3390/genes9070345

Chicago/Turabian Style

Yu, Jingwen, Pengyao Ping, Lei Wang, Linai Kuang, Xueyong Li, and Zhelun Wu. 2018. "A Novel Probability Model for LncRNA–Disease Association Prediction Based on the Naïve Bayesian Classifier" Genes 9, no. 7: 345. https://doi.org/10.3390/genes9070345

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