BACKGROUND The study of functional associations between ncRNAs and human diseases is a pivotal task of modern research to develop new and more effective therapeutic approaches. Nevertheless, it is not a trivial task since it involves entities of different types, such as microRNAs, lncRNAs or target genes whose expression also depends on endogenous or exogenous factors. Such a complexity can be faced by representing the involved biological entities and their relationships as a network and by exploiting network-based computational approaches able to identify new associations. However, existing methods are limited to homogeneous networks (i.e., consisting of only one type of objects and relationships) or can exploit only a small subset of the features of biological entities, such as the presence of a particular binding domain, enzymatic properties or their involvement in specific diseases. RESULTS To overcome the limitations of existing approaches, we propose the system LP-HCLUS, which exploits a multi-type hierarchical clustering method to predict possibly unknown ncRNA-disease relationships. In particular, LP-HCLUS analyzes heterogeneous networks consisting of several types of objects and relationships, each possibly described by a set of features, and extracts multi-type clusters that are subsequently exploited to predict new ncRNA-disease associations. The extracted clusters are overlapping, hierarchically organized, involve entities of different types, and allow LP-HCLUS to catch multiple roles of ncRNAs in diseases at different levels of granularity. Our experimental evaluation, performed on heterogeneous attributed networks consisting of microRNAs, lncRNAs, diseases, genes and their known relationships, shows that LP-HCLUS is able to obtain better results with respect to existing approaches. The biological relevance of the obtained results was evaluated according to both quantitative (i.e., TPR@k, Areas Under the TPR@k, ROC and Precision-Recall curves) and qualitative (i.e., according to the consultation of the existing literature) criteria. CONCLUSIONS The obtained results prove the utility of LP-HCLUS to conduct robust predictive studies on the biological role of ncRNAs in human diseases. The produced predictions can therefore be reliably considered as new, previously unknown, relationships among ncRNAs and diseases.

中文翻译：

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利用多类型层次聚类预测 ncRNA 与疾病之间的新关联。

背景研究 ncRNA 与人类疾病之间的功能关联是现代研究开发新的、更有效的治疗方法的关键任务。然而，这并不是一项微不足道的任务，因为它涉及不同类型的实体，例如 microRNA、lncRNA 或其表达也取决于内源或外源因素的靶基因。通过将所涉及的生物实体及其关系表示为网络并利用能够识别新关联的基于网络的计算方法，可以面对这种复杂性。然而，现有方法仅限于同质网络（即仅由一种类型的对象和关系组成）或只能利用生物实体特征的一小部分，例如特定结合域的存在、酶促特性或其参与特定疾病。结果 为了克服现有方法的局限性，我们提出了 LP-HCLUS 系统，该系统利用多类型层次聚类方法来预测可能未知的 ncRNA-疾病关系。特别是，LP-HCLUS 分析由多种类型的对象和关系组成的异构网络，每种对象和关系可能由一组特征描述，并提取随后用于预测新的 ncRNA 疾病关联的多类型簇。提取的簇是重叠的、分层组织的、涉及不同类型的实体，并且允许 LP-HCLUS 以不同的粒度级别捕获 ncRNA 在疾病中的多种作用。我们的实验评估是在由 microRNA、lncRNA、疾病、基因及其已知关系组成的异质属性网络上进行的，结果表明 LP-HCLUS 能够获得相对于现有方法更好的结果。根据定量（即TPR@k、TPR@k 下的面积、ROC 和精确召回曲线）和定性（即根据现有文献的查阅）标准评估所获得结果的生物学相关性。结论 所获得的结果证明 LP-HCLUS 可用于对 ncRNA 在人类疾病中的生物学作用进行可靠的预测研究。因此，所产生的预测可以被可靠地视为 ncRNA 与疾病之间新的、以前未知的关系。