Increasing microRNAs (miRNAs) have been confirmed to be inextricably linked to various diseases, and the discovery of their associations has become a routine way of treating diseases. To overcome the time-consuming and laborious shortcoming of traditional experiments in verifying the associations of miRNAs and diseases (MDAs), a variety of computational methods have emerged. However, these methods still have many shortcomings in terms of predictive performance and accuracy. In this study, a model based on multiple graph convolutional networks and random forest (MGCNRF) was proposed for the prediction MDAs. Specifically, MGCNRF first mapped miRNA functional similarity and sequence similarity, disease semantic similarity and target similarity, and the known MDAs into four different two-layer heterogeneous networks. Second, MGCNRF applied four heterogeneous networks into four different layered attention graph convolutional networks (GCNs), respectively, to extract MDA embeddings. Finally, MGCNRF integrated the embeddings of every MDA into the features of the miRNA-disease pair and predicted potential MDAs through the random forest (RF). Fivefold cross-validation was applied to verify the prediction performance of MGCNRF, which outperforms the other seven state-of-the-art methods by area under curve. Furthermore, the accuracy and the case studies of different diseases further demonstrate the scientific rationale of MGCNRF. In conclusion, MGCNRF can serve as a scientific tool for predicting potential MDAs.

In this study, we proposed a novel method called the graph capsule convolutional network (GCCN) to predict the progression from mild cognitive impairment to dementia and identify its pathogenesis. First, we proposed a novel risk gene discovery component to indirectly target genes with higher interactions with others. These risk genes and brain regions were collected as nodes to construct heterogeneous pathogenic information association graphs. Second, the graph capsules were established by projecting heterogeneous pathogenic information into a set of disentangled latent components. The orientation and length of capsules are representations of the format and intensity of pathogenic information. Third, graph capsule convolution network was used to model the information flows among pathogenic factors and elaborates the convergence of primary capsules to advanced capsules. The advanced capsule is a concept that organizes pathogenic information based on its consistency, and the synergistic effects of advanced capsules directed the development of the disease. Finally, discriminative pathogenic information flows were captured by a straightforward built-in interpretation mechanism, i.e., the dynamic routing mechanism, and applied to the identification of pathogenesis. GCCN has been experimentally shown to be significantly advanced on public datasets. Further experiments have shown that the pathogenic factors identified by GCCN are evidential and closely related to progressive mild cognitive impairment.

Community detection is a hot research direction of network science, which is of great importance to complex system analysis. Therefore, many community detection methods have been developed. Among them, evolutionary computation based ones with a single-objective function are promising in either benchmark or real data sets. However, they also encounter resolution limit problem in several scenarios. In this paper, a Multi-Objective Pigeon-Inspired Optimization (MOPIO) method is proposed for community detection with Negative Ratio Association (NRA) and Ratio Cut (RC) as its objective functions. In MOPIO, the genetic operator is used to redefine the representation and updating of pigeons. In each iteration, NRA and RC are calculated for each pigeon, and Pareto sorting scheme is utilized to judge non-dominated solutions for later crossover. A crossover strategy based on global and personal bests is designed, in which a compensation coefficient is developed to stably complete the work transition between the map and compass operator, and the landmark operator. When termination criteria were met, a leader selection strategy is employed to determine the final result from the optimal solution set. Comparison experiments of MOPIO, with MOPSO, MOGA-Net, Meme-Net and FN, are performed on real-world networks, and results indicate that MOPIO has better performance in terms of Normalized Mutual information and Adjusted Rand Index.