Lei Zhang

Extensive research indicates that microRNAs (miRNAs) play a crucial role in the analysis of complex human diseases. Recently, numerous methods utilizing graph neural networks have been developed to investigate the complex relationships between miRNAs and diseases. However, these methods often face challenges in terms of overall effectiveness and are sensitive to node positioning. To address these issues, the researchers introduce DARSFormer, an advanced deep learning model that integrates dynamic attention mechanisms with a spectral graph Transformer effectively. In the DARSFormer model, a miRNA-disease heterogeneous network is constructed initially. This network undergoes spectral decomposition into eigenvalues and eigenvectors, with the eigenvalue scalars being mapped into a vector space subsequently. An orthogonal graph neural network is employed to refine the parameter matrix. The enhanced features are then input into a graph Transformer, which utilizes a dynamic attention mechanism to amalgamate features by aggregating the enhanced neighbor features of miRNA and disease nodes. A projection layer is subsequently utilized to derive the association scores between miRNAs and diseases. The performance of DARSFormer in predicting miRNA-disease associations is exemplary. It achieves an AUC of 94.18% in a five-fold cross-validation on the HMDD v2.0 database. Similarly, on HMDD v3.2, it records an AUC of 95.27%. Case studies involving colorectal, esophageal, and prostate tumors confirm 27, 28, and 26 of the top 30 associated miRNAs against the dbDEMC and miR2Disease databases, respectively. The code and data for DARSFormer are accessible at https://github.com/baibaibaialone/DARSFormer .

Accurate traffic data forecasting is essential to improve the efficiency of intelligent transportation systems. Existing traffic prediction models only model spatial dependency based on the connectivity of roads, which overlooks the characteristic information of hidden spatial dependency and leads to a loss of prediction accuracy. In addition, there exists a strict relative positional relationship in the temporal dependency between traffic data, which is often overlooked by existing models, making it difficult to accurately model the temporal dependency. To solve these problems, this paper proposes a traffic data prediction method (MSS-STT) based on Multi-Spatial Scale Spatio-Temporal Transformer. MSS-STT first employs multiple specialized spatial Transformer networks to model different spatial scales in order to capture spatial dependencies and patterns at various levels. It also utilizes graph convolutional neural networks to extract static spatial structural features. Then, a gating mechanism is used to fuse the spatial dependencies from different spatial scales and the static spatial features. Finally, MSS-STT extracts different temporal dependencies by considering the order of time points and the varying contributions to the prediction from different relative positions between time points in historical traffic data. Experiments on three real-world datasets from the Caltrans Performance Measurement System (PeMS) show that the proposed MSS-STT model outperforms the state-of-the-art methods.

Background: A large number of evidences from biological experiments have confirmed that miRNAs play an important role in the progression and development of various human complex diseases. However, the traditional experiment methods are expensive and time-consuming. Therefore, it is a challenging task that how to develop more accurate and efficient methods for predicting potential associations between miRNA and disease.Results: In the study, we developed a computational model that combined heterogeneous graph convolutional network with enhanced layer for miRNA–disease association prediction (HGCNELMDA). The major improvement of our method lies in through restarting the random walk optimized the original features of nodes and adding a reinforcement layer to the hidden layer of graph convolutional network retained similar information between nodes in the feature space. In addition, the proposed approach recalculated the influence of neighborhood nodes on target nodes by introducing the attention mechanism. The reliable performance of the HGCNELMDA was certified by the AUC of 93.47% in global leave-one-out cross-validation (LOOCV), and the average AUCs of 93.01% in fivefold cross-validation. Meanwhile, we compared the HGCNELMDA with the state‑of‑the‑art methods. Comparative results indicated that o the HGCNELMDA is very promising and may provide a cost‑effective alternative for miRNA–disease association prediction. Moreover, we applied HGCNELMDA to 3 different case studies to predict potential miRNAs related to lung cancer, prostate cancer, and pancreatic cancer. Results showed that 48, 50, and 50 of the top 50 predicted miRNAs were supported by experimental association evidence. Therefore, the HGCNELMDA is a reliable method for predicting disease-related miRNAs.Conclusions: The results of the HGCNELMDA method in the LOOCV (leave-one-out cross validation, LOOCV) and 5-cross validations were 93.47% and 93.01%, respectively. Compared with other typical methods, the performance of HGCNELMDA is higher. Three cases of lung cancer, prostate cancer, and pancreatic cancer were studied. Among the predicted top 50 candidate miRNAs, 48, 50, and 50 were verified in the biological database HDMMV2.0. Therefore; this further confirms the feasibility and effectiveness of our method. Therefore, this further confirms the feasibility and effectiveness of our method. To facilitate extensive studies for future disease-related miRNAs research, we developed a freely available web server called HGCNELMDA is available athttp://124.221.62.44:8080/HGCNELMDA.jsp.

Background: Cumulative evidence from biological experiments has confirmed that miRNAs have significant roles to diagnose and treat complex diseases. However, traditional medical experiments have limitations in time-consuming and high cost so that they fail to find the unconfirmed miRNA and disease interactions. Thus, discovering potential miRNA-disease associations will make a contribution to the decrease of the pathogenesis of diseases and benefit disease therapy. Although, existing methods using different computational algorithms have favorable performances to search for the potential miRNA-disease interactions. We still need to do some work to improve experimental results.Results: We present a novel combined embedding model to predict MiRNA-disease associations (CEMDA) in this article. The combined embedding information of miRNA and disease is composed of pair embedding and node embedding. Compared with the previous heterogeneous network methods that are merely node-centric to simply compute the similarity of miRNA and disease, our method fuses pair embedding to pay more attention to capturing the features behind the relative information, which models the fine-grained pairwise relationship better than the previous case when each node only has a single embedding. First, we construct the heterogeneous network from supported miRNA-disease pairs, disease semantic similarity and miRNA functional similarity. Given by the above heterogeneous network, we find all the associated context paths of each confirmed miRNA and disease. Meta-paths are linked by nodes and then input to the gate recurrent unit (GRU) to directly learn more accurate similarity measures between miRNA and disease. Here, the multi-head attention mechanism is used to weight the hidden state of each meta-path, and the similarity information transmission mechanism in a meta-path of miRNA and disease is obtained through multiple network layers. Second, pair embedding of miRNA and disease is fed to the multi-layer perceptron (MLP), which focuses on more important segments in pairwise relationship. Finally, we combine meta-path based node embedding and pair embedding with the cost function to learn and predict miRNA-disease association. The source code and data sets that verify the results of our research are shown athttps://github.com/liubailong/CEMDA.Conclusions: The performance of CEMDA in the leave-one-out cross validation and fivefold cross validation are 93.16% and 92.03%, respectively. It denotes that compared with other methods, CEMDA accomplishes superior performance. Three cases with lung cancers, breast cancers, prostate cancers and pancreatic cancers show that 48,50,50 and 50 out of the top 50 miRNAs, which are confirmed in HDMM V2.0. Thus, this further identifies the feasibility and effectiveness of our method.