Analyzing the rich information behind heterogeneous networks through network representation learning methods is signifcant for many application tasks such as link prediction, node classifcation and similarity research. As the networks evolve over times, the interactions among the nodes in networks make heterogeneous networks exhibit dynamic characteristics. However, almost all the existing heterogeneous network representation learning methods focus on static networks which ignore dynamic characteristics. In this paper, we propose a novel approach DHNE to learn the representations of nodes in dynamic heterogeneous networks. The key idea of our approach is to construct comprehensive historical-current networks based on subgraphs of snapshots in time step to capture both the historical and current information in the dynamic heterogeneous network. And then under the guidance of meta paths, DHNE performs random walks on the constructed historical-current graphs to capture semantic information. After getting the node sequences through random walks, we propose the dynamic heterogeneous skip-gram model to learn the embeddings. Experiments on large-scale real-world networks demonstrate that the embeddings learned by the proposed DHNE model achieve better performances than state-of-the-art methods in various downstream tasks including node classifcation and visualization. INDEX TERMS Dynamic heterogeneous networks, network representation learning, random walk, skip-gram model.

The frailty index and the epigenetic age models as biological age metrics ABSTRACT A reliable model of biological age is instrumental in geriatrics and gerontology. It should account for heterogeneity and plasticity of aging. It should also accurately predict aging-related adverse outcomes. Epigenetic age models are based on DNA methylation levels of selected genomic sites. Some of the epigenetic age models are significant predictors of mortality and healthy/unhealthy aging. However, biological function of DNA methylation at the selected sites is yet to be determined. Frailty is viewed as a syndrome resulting from declined physiological reserve and resilience. The frailty index is a probability-based extension of the frailty concept.Simply being the proportion of health deficits, the frailty index quantifies the progression of unhealthy aging. The frailty index is the best predictor of mortality. It is associated with various biological factors, providing insight into biological processes of aging. Investigation of multiomics factors associated with the frailty index will provide further insight.

In this paper, we investigate the effect of different hyperparameters as well as different combinations of hyperparameters settings on the performance of the Attention-Gated Convolutional Neural Networks (AGCNNs), e.g., the kernel window size, the number of feature maps, the keep rate of the dropout layer, and the activation function. We draw practical advice from a wide range of empirical results. Through the sensitivity analysis, we further improve the hyperparameters settings of AGCNNs. Experiments show that our proposals could achieve an average of 0.81% and 0.67% improvements on AGCNN-NLReLU-rand and AGCNN-SELU-rand, respectively; and an average of 0.47% and 0.45% improvements on AGCNN-NLReLU-static and AGCNN-SELU-static, respectively.

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