BPLLDA: Predicting lncRNA-Disease Associations Based on Simple Paths With Limited Lengths in a Heterogeneous Network.
BPLLDA: Predicting lncRNA-Disease Associations Based on Simple Paths With Limited Lengths in a Heterogeneous Network.
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BPLLDA：基于异构网络中长度有限的简单路径预测 lncRNA 疾病关联
DOI：
10.3389/fgene.2018.00411
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发表时间：
2018
影响因子：
3.7
通讯作者：
Yang J
中科院分区：
文献类型：
--
作者：
Xiao X;Zhu W;Liao B;Xu J;Gu C;Ji B;Yao Y;Peng L;Yang J
近年来，人们越来越清楚，长链非编码RNA（lncRNA）在许多与人类疾病相关的生物学过程中起着关键作用。推断潜在的lncRNA-疾病关联对于揭示疾病背后的秘密、开发新药和优化个性化治疗至关重要。然而，验证lncRNA-疾病关联的生物学实验非常耗时且昂贵。因此，开发有效的计算模型至关重要。在这项研究中，我们提出了一种称为BPLLDA的方法来预测lncRNA-疾病关联的基础上，在一个异构的lncRNA-疾病关联网络的固定长度的路径。具体而言，BPLLDA首先通过整合lncRNA-疾病关联网络、lncRNA功能相似性网络和疾病语义相似性网络构建了一个异质性lncRNA-疾病网络。然后，它根据连接它们的路径及其在网络中的长度来推断lncRNA-疾病关联的概率。与现有方法相比，BPLLDA具有一些优势，包括不需要阴性样本以及预测与新型lncRNA或新型疾病相关的关联的能力。将BPLLDA与两种流行的方法LRLSLDA和GrwLDA一起应用于称为LncRNADisease的典型lncRNA-disease关联数据库。BPLLDA的受试者工作特征曲线下的留一交叉验证面积分别为0.87117、0.82403和0.78528，用于预测总体关联、与新lncRNA相关的关联和与新疾病相关的关联，高于两种比较方法。此外，选择宫颈癌、神经胶质瘤和非小细胞肺癌作为病例研究，最近发表的文献验证了预测的前五名lncRNA-疾病关联。总之，BPLLDA在预测新的lncRNA-疾病关联以及与新的lncRNA和疾病相关的关联方面表现出良好的性能。它可能有助于理解lncRNA相关疾病，如某些癌症。
In recent years, it has been increasingly clear that long noncoding RNAs (lncRNAs) play critical roles in many biological processes associated with human diseases. Inferring potential lncRNA-disease associations is essential to reveal the secrets behind diseases, develop novel drugs, and optimize personalized treatments. However, biological experiments to validate lncRNA-disease associations are very time-consuming and costly. Thus, it is critical to develop effective computational models. In this study, we have proposed a method called BPLLDA to predict lncRNA-disease associations based on paths of fixed lengths in a heterogeneous lncRNA-disease association network. Specifically, BPLLDA first constructs a heterogeneous lncRNA-disease network by integrating the lncRNA-disease association network, the lncRNA functional similarity network, and the disease semantic similarity network. It then infers the probability of an lncRNA-disease association based on paths connecting them and their lengths in the network. Compared to existing methods, BPLLDA has a few advantages, including not demanding negative samples and the ability to predict associations related to novel lncRNAs or novel diseases. BPLLDA was applied to a canonical lncRNA-disease association database called LncRNADisease, together with two popular methods LRLSLDA and GrwLDA. The leave-one-out cross-validation areas under the receiver operating characteristic curve of BPLLDA are 0.87117, 0.82403, and 0.78528, respectively, for predicting overall associations, associations related to novel lncRNAs, and associations related to novel diseases, higher than those of the two compared methods. In addition, cervical cancer, glioma, and non-small-cell lung cancer were selected as case studies, for which the predicted top five lncRNA-disease associations were verified by recently published literature. In summary, BPLLDA exhibits good performances in predicting novel lncRNA-disease associations and associations related to novel lncRNAs and diseases. It may contribute to the understanding of lncRNA-associated diseases like certain cancers.