Department of Mathematics and Computer Science, University of Catania , Catania , Italy.
Department of Clinical and Experimental Medicine, University of Catania , Catania , Italy.
Front Bioeng Biotechnol. 2014 Dec 12;2:71. doi: 10.3389/fbioe.2014.00071. eCollection 2014.
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. (2014) 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.
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. (2014), 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.
All the ncPred predictions together with the datasets used for the analysis are available at the following url: http://alpha.dmi.unict.it/ncPred/
在过去的几年中，实验证据强调了 microRNAs 对人类疾病的作用。miRNAs 对细胞过程的调节至关重要，因此它们的异常可能是病理现象的触发原因之一。它们只是非编码 RNA 大家族中的一个成员，其中包括转录的超保守区域 (T-UCRs)、小核仁 RNA (snoRNAs)、PIWI 相互作用 RNA (piRNAs)、大基因间非编码 RNA (lincRNAs) 和异质长非编码 RNA (lncRNAs)。它们与疾病的关联数量很少，其可靠性也值得怀疑。在文献中，只有 Yang 等人 (2014) 最近提出了一种预测 lncRNA-疾病关联的方法。然而，该技术在预测质量方面存在不足。所有这些因素都需要研究新的生物信息学工具来预测高质量的 ncRNA-疾病关联。在这里，我们提出了一种名为 ncPred 的方法，用于基于推荐技术推断新型 ncRNA-疾病关联。我们通过一个三分网络来表示我们的知识，该网络的节点是 ncRNAs、靶标或疾病。网络中的相互作用通过其靶标将每个 ncRNA 与一种疾病联系起来。我们的算法从这样一个网络开始，使用多级资源转移技术计算每个 ncRNA-疾病对之间的权重，在每一步都考虑前一步转移的资源。
我们的实验分析结果表明，与 Yang 等人 (2014) 相比，我们的方法能够预测更具生物学意义的关联，从而提高了 ROC 曲线下的平均面积 (AUC)。这些结果证明了我们的方法预测具有生物学意义的关联的能力，这可能有助于更好地理解复杂疾病中涉及的分子过程。
所有 ncPred 预测结果以及用于分析的数据集均可在以下网址获得：http://alpha.dmi.unict.it/ncPred/
