Prediction of New Associations between ncRNAs and Diseases Exploiting Multi-Type Hierarchical Clustering

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. Such 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 or can exploit only a limited set of the features of biological entities. To overcome the limitations of existing approaches, we proposed the system LP-HCLUS, which analyzes heterogeneous networks consisting of several types of objects and relationships, each possibly described by a set of features, and extracts hierarchically organized, possibly overlapping, multi-type clusters that are subsequently exploited to predict new ncRNA-disease associations. Our experimental evaluation shows that, according to both quantitative (i.e., TPR@k, ROC and PR curves) and qualitative criteria, LP-HCLUS produces better results.