Ruiqi Qin

Disease similarity analysis impacts significantly in pathogenesis revealing, treatment recommending, and disease-causing genes predicting. Previous works study the disease similarity based on the semantics obtaining from biomedical ontologies (e.g., disease ontology) or the function of disease-causing molecules. However, such methods almost focus on a single perspective for obtaining disease features, which may lead to biased results for similar disease detection. To address this issue, we propose a disease information network-based integrate approach named MISSION for detecting similar diseases. By leveraging the associations between diseases and other biomedical entities, the disease information network is established firstly. And then, the disease similarity features extracted from the aspects of disease taxonomy, attributes, literature, and annotations are integrated into the disease information network. Finally, the top-k similar disease query is performed based on the integrative disease information. The experiments conducted on real-world datasets demonstrate that MISSION is effective and useful in similar disease detection.

Analysis of gene similarity not only can provide information on the understanding of the biological roles and functions of a gene, but may also reveal the relationships among various genes. In this paper, we introduce a novel idea of mining similar aspects from a gene information network, i.e., for a given gene pair, we want to know in which aspects (meta paths) they are most similar from the perspective of the gene information network. We defined a similarity metric based on the set of meta paths connecting the query genes in the gene information network and used the rank of similarity of a gene pair in a meta path set to measure the similarity significance in that aspect. A minimal set of gene meta paths where the query gene pair ranks the highest is a similar aspect, and the similar aspect of a query gene pair is far from trivial. We proposed a novel method, SCENARIO, to investigate minimal similar aspects. Our empirical study on the gene information network, constructed from six public gene-related databases, verified that our proposed method is effective, efficient, and useful.

To identify similar diseases has significant implications for revealing the etiology and pathogenesis of diseases and further research in the domain of biomedicine. Currently most methods for the measurement of disease similarity utilize either associations of ontological disease concepts or functional interactions between disease-related genes. These methods are heavily dependent on the ontology, which are not always available, and the selection of datasets. Moreover, many methods suffer from a drawback that they only use a single metric to evaluate disease similarity from an individual data source, which may result in biased conclusions without consideration of other aspects. In this study, we proposed a novel ontology-independent framework, namely RADAR, for learning representations for diseases to deduce their similarities from an integrative perspective. By leveraging the associations between diseases and disease-related biomedical entities, a disease similarity network was built under various metrics. Then a multi-layer disease similarity network was constructed by integrating multiple disease similarity networks derived from multiple data sources, where the representation learning was derived to provide a comprehensive evaluation of disease similarities. The performance of RADAR was assessed by a benchmark disease set and 100 random disease sets. Experimental results demonstrated that RADAR can detect similar diseases effectively.