ncPred: ncRNA-Disease Association Prediction through Tripartite Network-Based Inference.
ncPred: ncRNA-Disease Association Prediction through Tripartite Network-Based Inference.
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DOI：
10.3389/fbioe.2014.00071
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发表时间：
2014
影响因子：
5.7
通讯作者：
Pulvirenti A
中科院分区：
文献类型：
--
作者：
Alaimo S;Giugno R;Pulvirenti A
动机：在过去几年中，实验证据凸显了微小核糖核酸（microRNAs）对人类疾病的作用。微小核糖核酸对细胞过程的调控至关重要，因此，它们的异常可能是病理现象的触发原因之一。它们只是一大类非编码核糖核酸中的一员，这类非编码核糖核酸包括转录的超保守区域（T - UCRs）、小核仁核糖核酸（snoRNAs）、PIWI相互作用核糖核酸（piRNAs）、长基因间非编码核糖核酸（lincRNAs）以及异质性的长非编码核糖核酸（lncRNAs）组。它们与疾病的关联数量很少，并且其可靠性存疑。在文献中，杨等人最近只提出了一种预测长非编码核糖核酸 - 疾病关联的方法。然而，这种技术在预测质量上有所欠缺。所有这些因素都表明需要研究新的生物信息学工具来预测高质量的非编码核糖核酸 - 疾病关联。在此，我们提出一种基于推荐技术的名为ncPred的方法，用于推断新的非编码核糖核酸 - 疾病关联。我们通过一个三方网络来表示我们的知识，该网络的节点是非编码核糖核酸、靶点或疾病。这种网络中的相互作用通过靶点将每种非编码核糖核酸与一种疾病相关联。我们的算法从这样一个网络出发，使用一种多级资源转移技术计算每对非编码核糖核酸 - 疾病之间的权重，该技术在每一步都考虑到前一步转移的资源。
结果：我们的实验分析结果表明，相对于杨等人所获得的结果，我们的方法能够预测出更具生物学意义的关联，在受试者工作特征曲线（ROC曲线）下的平均面积（AUC）方面有所提高。这些结果证明了我们的方法预测具有生物学意义关联的能力，这可能会使人们更好地理解复杂疾病中所涉及的分子过程。
可用性：所有ncPred的预测结果以及用于分析的数据集可在以下网址获取：
Motivation: Over the past few years, experimental evidence has highlighted the role of microRNAs to human diseases. miRNAs are critical for the regulation of cellular processes, and, therefore, their aberration can be among the triggering causes of pathological phenomena. They are just one member of the large class of non-coding RNAs, which include transcribed ultra-conserved regions (T-UCRs), small nucleolar RNAs (snoRNAs), PIWI-interacting RNAs (piRNAs), large intergenic non-coding RNAs (lincRNAs) and, the heterogeneous group of long non-coding RNAs (lncRNAs). Their associations with diseases are few in number, and their reliability is questionable. In literature, there is only one recent method proposed by Yang et al. to predict lncRNA-disease associations. This technique, however, lacks in prediction quality. All these elements entail the need to investigate new bioinformatics tools for the prediction of high quality ncRNA-disease associations. Here, we propose a method called ncPred for the inference of novel ncRNA-disease association based on recommendation technique. We represent our knowledge through a tripartite network, whose nodes are ncRNAs, targets, or diseases. Interactions in such a network associate each ncRNA with a disease through its targets. Our algorithm, starting from such a network, computes weights between each ncRNA-disease pair using a multi-level resource transfer technique that at each step takes into account the resource transferred in the previous one. Results: The results of our experimental analysis show that our approach is able to predict more biologically significant associations with respect to those obtained by Yang et al., yielding an improvement in terms of the average area under the ROC curve (AUC). These results prove the ability of our approach to predict biologically significant associations, which could lead to a better understanding of the molecular processes involved in complex diseases. Availability: All the ncPred predictions together with the datasets used for the analysis are available at the following url: