Long noncoding RNAs (lncRNAs) play an important role in the occurrence and development of diseases. Predicting disease-related lncRNAs can help to understand the pathogenesis of diseases deeply. The existing methods mainly rely on multi-source data related to lncRNAs and diseases when predicting the associations between lncRNAs and diseases. There are interdependencies among node attributes in a heterogeneous graph composed of all lncRNAs, diseases and micro RNAs. The meta-paths composed of various connections between them also contain rich semantic information. However, the existing methods neglect to integrate attribute information of intermediate nodes in meta-paths. We propose a novel association prediction model, GSMV, to learn and deeply integrate the global dependencies, semantic information of meta-paths and node-pair multi-view features related to lncRNAs and diseases. We firstly formulate the global representations of the lncRNA and disease nodes by establishing a self-attention mechanism to capture and learn the global dependencies among node attributes. Second, starting from the lncRNA and disease nodes, respectively, multiple meta-pathways are established to reveal different semantic information. Considering that each meta-path contains specific semantics and has multiple meta-path instances which have different contributions to revealing meta-path semantics, we design a graph neural network based module which consists of a meta-path instance encoding strategy and two novel attention mechanisms. The proposed meta-path instance encoding strategy is used to learn the contextual connections between nodes within a meta-path instance. One of the two new attention mechanisms is at the meta-path instance level, which learns rich and informative meta-path instances. The other attention mechanism integrates various semantic information from multiple meta-paths to learn the semantic representation of lncRNA and disease nodes. Finally, a dilated convolution-based learning module with adjustable receptive fields is proposed to learn multi-view features of lncRNA-disease node pairs. The experimental results prove that our method outperforms seven state-of-the-art comparing methods for lncRNA-disease association prediction. Ablation experiments demonstrate the contributions of the proposed global representation learning, semantic information learning, pairwise multi-view feature learning and the meta-path instance encoding strategy. Case studies on three cancers further demonstrate our method’s ability to discover potential disease-related lncRNA candidates. zhang@hlju.edu.cn or peiliangwu@ysu.edu.cn Supplementary data are available at Briefings in Bioinformatics online.

The microbes in human body play a crucial role in influencing the functions of drugs, as they can regulate the activities and toxicities of drugs. Most recent methods for predicting drug-microbe associations are based on graph learning. However, the relationships among multiple drugs and microbes are complex, diverse and heterogeneous. Existing methods often fail to fully model the relationships. In addition, the attributes of drug-microbe pairs exhibit long-distance spatial correlations, which previous methods have not integrated effectively. We propose a new prediction method named DHDMP which is designed to encode the relationships among multiple drugs and microbes and integrate the attributes of various neighbor nodes along with the pairwise long-distance correlations. First, we construct a hypergraph with dynamic topology, where each hyperedge represents a specific relationship among multiple drug nodes and microbe nodes. Considering the heterogeneity of node attributes across different categories, we developed a node category-sensitive hypergraph convolution network to encode these diverse relationships. Second, we construct homogeneous graphs for drugs and microbes respectively, as well as drug-microbe heterogeneous graph, facilitating the integration of features from both homogeneous and heterogeneous neighbors of each target node. Third, we introduce a graph convolutional network with cross-graph feature propagation ability to transfer node features from homogeneous to heterogeneous graphs for enhanced neighbor feature representation learning. The propagation strategy aids in the deep fusion of features from both types of neighbors. Finally, we design spatial cross-attention to encode the attributes of drug-microbe pairs, revealing long-distance correlations among multiple pairwise attribute patches. The comprehensive comparison experiments showed our method outperformed state-of-the-art methods for drug-microbe association prediction. The ablation studies demonstrated the effectiveness of node category-sensitive hypergraph convolution network, graph convolutional network with cross-graph feature propagation, and spatial cross-attention. Case studies on 3 drugs further showed DHDMP’s potential application in discovering the reliable candidate microbes for the interested drugs. Source codes and supplementary materials are available at https://github.com/pingxuan-hlju/DHDM. Supplementary data are available at Bioinformatics online.

We present a prediction framework to estimate the remaining useful life (RUL) of equipment based on the generative adversarial imputation net (GAIN) and multiscale deep convolutional neural network and long short‐term memory (MSDCNN‐LSTM). The method we proposed addresses the problem of missing data caused by sensor failures in engineering applications. First, a binary matrix is used to adjust the proportion of “0” to simulate the number of missing data in the engineering environment. Then, the GAIN model is used to impute the missing data and approximate the true sample distribution. Finally, the MSDCNN‐LSTM model is used for RUL prediction. Experiments are carried out on the commercial modular aero‐propulsion system simulation (C‐MAPSS) dataset to validate the proposed method. The prediction results show that the proposed method outperforms other methods when packet loss occurs, showing significant improvements in the root mean square error (RMSE) and the score function value.