Yu Jingwen, Ping Pengyao, Wang Lei, Kuang Linai, Li Xueyong, Wu Zhelun
Key Laboratory of Intelligent Computing & Information Processing, Xiangtan University, Xiangtan 411105, China.
College of Computer Engineering & Applied Mathematics, Changsha University, Changsha 410001, China.
Genes (Basel). 2018 Jul 8;9(7):345. doi: 10.3390/genes9070345.
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.
越来越多的研究表明，长链非编码RNA（lncRNA）在生物过程、复杂疾病的诊断、预后和治疗中发挥着关键作用。然而，lncRNA与疾病之间经实验验证的关联仍然非常有限。最近，通过整合多种异质生物数据，已开发出计算模型来发现lncRNA与疾病之间的潜在关联；这已成为生物学研究中的一个热门话题。在本文中，我们通过整合包括miRNA-疾病、miRNA-lncRNA和lncRNA-疾病关联及相互作用在内的多种生物信息，构建了一个全局三方网络。然后，我们通过将基因-lncRNA相互作用、基因-疾病关联和基因-miRNA相互作用网络附加到全局三方网络上，构建了一个全局四方网络。随后，基于这两个全局网络，提出了一种基于朴素贝叶斯分类器的新方法来预测潜在的lncRNA-疾病关联（NBCLDA）。与现有方法相比，我们的新方法并不完全依赖于已知的lncRNA-疾病关联，并且在留一法交叉验证中通过有效的ROC曲线下面积（AUC）能够实现可靠的性能。此外，为了进一步评估NBCLDA的性能，本文对结直肠癌、前列腺癌和神经胶质瘤进行了案例研究，模拟结果表明NBCLDA未来可能成为生物医学研究的一个优秀工具。
