DualRank: multiplex networkbased dual ranking for heterogeneous complex disease analysis Analysis of heterogeneous complex diseases based on the expression of biomarkers has always been the focus of medical research. In the past studies, the powerful network propagation has been applied in finding marker genes related to specific diseases. However, the network propagation model largely depends on the reliability and integrity of the network data, current networks may cause some problems due to the incompleteness of the networks. In this study, we developed a multiplex networkbased dual ranking framework (DualRank) for heterogeneous complex disease analysis. We applied the proposed method to heterogeneous complex diseases for disease diagnosis, cancer prognosis, and similar disease classification. The results showed that DualRank outperformed current methods and could identify biomarkers with small quantity, strong prediction accuracy and biological interpretability.

Disease Gene Prediction Based on Heterogeneous Probabilistic Hypergraph Ranking In order to save time and cost, many disease gene prediction methods have been proposed in recent years. However, the traditional network model uses a binary relationship to represent the relationship between different proteins or gene molecules and phenotypes, which leads to the loss of information. Recently, hypergraph shows that it can overcome this loss of information to some extent and preserve the multivariate relationship, so we transformed the disease gene prediction problem into the problem of ranking the multivariaterelationship object. In this paper, we propose a method of Heterogeneous Probabilistic Hypergraph Ranking (HPHR) to predict disease genes. Firstly, fix a graph centroid for each hyperedge and according to different associations, and add other nodes related to the graph centroid to hyperedges with a certain probability. Then transform the problem of predicting disease genes into the problem of ranking heterogeneous objects, and the candidate genes are sorted by hypergraph ranking. The method is then applied to the integrated disease gene network. Compared with other prediction methods achieved better results, which was verified by this experiment.