Predicting miRNA-Disease Associations by Combining Graph and Hypergraph Convolutional Network.

Geometric deep learning provides a principled and universal way for the integration of imaging and non-imaging modes in the medical field. Graph Convolutional Networks (GCNs) have been widely explored in a variety of problems, such as disease prediction, segmentation, and matrix completion. Using large, multi-modal data sets, graphs can capture the interaction of individual elements represented as nodes in the graphs. In particular, in medical applications, nodes can represent individuals (patients or healthy controls) in a potentially large population and are accompanied by a set of features, while the edges of the graph contain the associations between subjects in an intuitive way. This representation allows the inclusion of rich imaging and non-imaging information as well as individual subject characteristics in the task of disease classification. This article gives an overview of graph convolutional neural networks, focusing on the application of graph convolutional neural networks in disease prediction, and discusses the challenges and prospects of graph convolutional neural networks in disease prediction.

Automatic building extraction from optical imagery remains a challenge due to, for example, the complexity of building shapes. Semantic segmentation is an efficient approach for this task. The latest development in deep convolutional neural networks (DCNNs) has made accurate pixel-level classification tasks possible. Yet one central issue remains: the precise delineation of boundaries. Deep architectures generally fail to produce fine-grained segmentation with accurate boundaries due to their progressive down-sampling. Hence, we introduce a generic framework to overcome the issue, integrating the graph convolutional network (GCN) and deep structured feature embedding (DSFE) into an end-to-end workflow. Furthermore, instead of using a classic graph convolutional neural network, we propose a gated graph convolutional network, which enables the refinement of weak and coarse semantic predictions to generate sharp borders and fine-grained pixel-level classification. Taking the semantic segmentation of building footprints as a practical example, we compared different feature embedding architectures and graph neural networks. Our proposed framework with the new GCN architecture outperforms state-of-the-art approaches. Although our main task in this work is building footprint extraction, the proposed method can be generally applied to other binary or multi-label segmentation tasks.

Video-based human action recognition is one of the most important and challenging areas of research in the field of computer vision. Human action recognition has found many pragmatic applications in video surveillance, human-computer interaction, entertainment, autonomous driving, etc. Owing to the recent development of deep learning methods for human action recognition, the performance of action recognition has significantly enhanced for challenging datasets. Deep learning techniques are mainly used for recognizing actions in images and videos comprising of Euclidean data. A recent development in deep learning methods is the extension of these techniques to non-Euclidean data or graph data with many nodes and edges. Human body skeleton resembles a graph, therefore, the graph convolutional network (GCN) is applicable to the non-Euclidean body skeleton. In the past few years, GCN has emerged as an important tool for skeleton-based action recognition. Therefore, we conduct a survey using GCN methods for action recognition. Herein, we present a comprehensive overview of recent GCN techniques for action recognition, propose a taxonomy for the categorization of GCN techniques for action recognition, carry out a detailed study of the benchmark datasets, enlist relevant resources and open-source codes, and finally provide an outline for future research directions and trends. To the best of authors' knowledge, this is the first survey for action recognition using GCN techniques.

Exploring the optimization of communication strategies for animation films in the context of cross-cultural communication, this research integrates the Internet of Things (IoT) and convolutional networks. The research constructs a collaborative filtering (CF) movie recommendation model based on a graph convolutional neural network (GCN) and investigates its application in cross-cultural communication. The fusion of IoT and convolutional networks in movie communication is also analyzed, and the effectiveness of the proposed GCN-CF model is validated through comparative experiments. The results indicate that, compared to other models, the GCN-CF model achieves the lowest Root Mean Square Error (RMSE) on the MovieLens 100 K and MovieLens 1 M datasets, with values of 0.8762 and 0.8275, respectively. Compared to traditional models, the GCN-CF model exhibits significantly superior performance in terms of RMSE, with reductions ranging from 0.6 to 5.2%, highlighting its heightened detection accuracy and overall performance. Moreover, the performance of the GCN-CF model is enhanced after introducing attention mechanisms and auxiliary information on both datasets, showing an improvement of 0.4% compared to the scenario without these additions. This data demonstrates the effectiveness of attention mechanisms and auxiliary information. Finally, the research presents an animation film communication strategy based on IoT and convolutional networks, offering novel insights for film production and communication, along with positive implications for cultural exchange and the advancement of the global media industry.

LightGCAN: A lightweight graph convolutional attention network for user preference modeling and personalized recommendation

Cross-subject emotion recognition in brain-computer interface based on frequency band attention graph convolutional adversarial neural networks

Accurate identification of the miRNA-disease associations (MDAs) helps to understand the etiology and mechanisms of various diseases. However, the experimental methods are costly and time-consuming. Thus, it is urgent to develop computational methods towards the prediction of MDAs. Based on the graph theory, the MDA prediction is regarded as a node classification task in the present study. To solve this task, we propose a novel method MDA-GCNFTG, which predicts MDAs based on Graph Convolutional Networks (GCNs) via graph sampling through the Feature and Topology Graph to improve the training efficiency and accuracy. This method models both the potential connections of feature space and the structural relationships of MDA data. The nodes of the graphs are represented by the disease semantic similarity, miRNA functional similarity and Gaussian interaction profile kernel similarity. Moreover, we considered six tasks simultaneously on the MDA prediction problem at the first time, which ensure that under both balanced and unbalanced sample distribution, MDA-GCNFTG can predict not only new MDAs but also new diseases without known related miRNAs and new miRNAs without known related diseases. The results of 5-fold cross-validation show that the MDA-GCNFTG method has achieved satisfactory performance on all six tasks and is significantly superior to the classic machine learning methods and the state-of-the-art MDA prediction methods. Moreover, the effectiveness of GCNs via the graph sampling strategy and the feature and topology graph in MDA-GCNFTG has also been demonstrated. More importantly, case studies for two diseases and three miRNAs are conducted and achieved satisfactory performance.

Vehicle trajectory prediction is an important research basis for the decision making and path planning of the intelligent and connected vehicle. In the connected vehicle environment, vehicles share information and drive cooperatively, and the intelligent and connected vehicles are able to obtain more accurate and rich perception information, which provides a data basis for accurate prediction of vehicle trajectories. However, attaining accurate and effective vehicle trajectory predictions poses technical challenges due to insufficient extraction of vehicular spatial–temporal interaction features. In this paper, we propose a vehicle trajectory prediction model based on graph convolutional neural network (GCN) in a connected vehicle environment. Specifically, using the driving scene information obtained by the intelligent and connected vehicle, the spatial graph and temporal graph are constructed based on the spatial interaction coefficient (SIC) and self-attention mechanism, respectively. Then, the graph data are entered into the interaction extraction module, and the spatial interaction features and temporal interaction features are extracted separately using the graph convolutional networks, which are fused to obtain the spatial–temporal interaction information. Finally, the interaction features are learned based on the convolutional neural networks to output the future trajectory information of all vehicles in the scene by one forward operation rather than a step-by-step process. The ablation experiment results show that the method proposed in this study to model the spatiotemporal interaction among vehicles based on SIC and self-attention mechanism reduces the prediction error by 5% and 12%, respectively. The results from the model comparison experiment show that the proposed method engenders an 8% improvement in prediction accuracy over the state-of-the-art solution, providing technical and theoretical support for trajectory prediction research of intelligent and connected vehicles.

Identifying the relationships among long non-coding RNAs (lncRNAs), microRNAs (miRNAs) and diseases is highly valuable for diagnosing, preventing, treating and prognosing diseases. The development of effective computational prediction methods can reduce experimental costs. While numerous methods have been proposed, they often to treat the prediction of lncRNA-disease associations (LDAs), miRNA-disease associations (MDAs) and lncRNA-miRNA interactions (LMIs) as separate task. Models capable of predicting all three relationships simultaneously remain relatively scarce. Our aim is to perform multi-task predictions, which not only construct a unified framework, but also facilitate mutual complementarity of information among lncRNAs, miRNAs and diseases. In this work, we propose a novel unsupervised embedding method called graph contrastive learning for multi-task prediction (GCLMTP). Our approach aims to predict LDAs, MDAs and LMIs by simultaneously extracting embedding representations of lncRNAs, miRNAs and diseases. To achieve this, we first construct a triple-layer lncRNA-miRNA-disease heterogeneous graph (LMDHG) that integrates the complex relationships between these entities based on their similarities and correlations. Next, we employ an unsupervised embedding model based on graph contrastive learning to extract potential topological feature of lncRNAs, miRNAs and diseases from the LMDHG. The graph contrastive learning leverages graph convolutional network architectures to maximize the mutual information between patch representations and corresponding high-level summaries of the LMDHG. Subsequently, for the three prediction tasks, multiple classifiers are explored to predict LDA, MDA and LMI scores. Comprehensive experiments are conducted on two datasets (from older and newer versions of the database, respectively). The results show that GCLMTP outperforms other state-of-the-art methods for the disease-related lncRNA and miRNA prediction tasks. Additionally, case studies on two datasets further demonstrate the ability of GCLMTP to accurately discover new associations. To ensure reproducibility of this work, we have made the datasets and source code publicly available at https://github.com/sheng-n/GCLMTP.

In this paper, we propose a model using a graph convolution network for predicting the accuracy of the automatically generated convolution neural network (CNN). In recent years, there have been many excellent results in the automatic designing of deep learning network structures and their hyperparameters. These works mainly use evolutionary algorithms and reinforcement learning methods. But to verify the accuracy of newly generated neural network samples, it requires a lot of computational time and large scale of hardware. Therefore, how to reduce training time and improve the utilization of samples has become an important problem of automatic design for deep learning networks. On the other hand, the effectiveness of the graph convolution network (GCN) has been shown in processing graph data on the tasks of graph node classification, link prediction, and clustering. We are considering using GCN to predict the accuracy of the automatically designed neural networks. We compare GCN, a multi-layer neural network(NN), and CNN on the accuracy prediction task of automatically generated CNN for image classification tasks on the CIFAR-10 dataset. Our experiment demonstrates the advantages of GCN on small datasets compared to the multi-layer neural network and CNN, while GCN achieves the equivalent accuracy to the other methods on large datasets.

In recent years, many experiments have proved that microRNAs (miRNAs) play a variety of important regulatory roles in cells, and their abnormal expression can lead to the emergence of specific diseases. Therefore, it is greatly valuable to do research on the association between miRNAs and diseases, which can effectively help prevent and treat miRNA-related diseases. At present, effective computational methods still need to be developed to better identify potential miRNA-disease associations. Inspired by graph convolutional networks, in this study, we propose a new method based on Attention aware Multi-view similarity networks and Hypergraph learning for MiRNA-Disease Associations identification (AMHMDA). First, we construct multiple similarity networks for miRNAs and diseases, and exploit the graph convolutional networks fusion attention mechanism to obtain the important information from different views. Then, in order to obtain high-quality links and richer nodes information, we introduce a kind of virtual nodes called hypernodes to construct heterogeneous hypergraph of miRNAs and diseases. Finally, we employ the attention mechanism to fuse the outputs of graph convolutional networks, predicting miRNA-disease associations. To verify the effectiveness of this method, we carry out a series of experiments on the Human MicroRNA Disease Database (HMDD v3.2). The experimental results show that AMHMDA has good performance compared with other methods. In addition, the case study results also fully demonstrate the reliable predictive performance of AMHMDA.

Detecting anomalous traffic behaviors with seasonal deep Kalman filter graph convolutional neural networks

Graph convolutional neural networks have difficulties in model training takes a lot of time and lack of universality in meta-path design when processing knowledge graphs. To solve these issues, this paper proposes an efficient recommendation algorithm integrating knowledge graphs with graph convolutional networks(EKG-GCN). The algorithm is mainly based on the graph convolution algorithm. Firstly, the relationship between users and entities is standardized and scored, then a new neighborhood aggregation method is proposed. Some nodes in the knowledge graph are set as central nodes. A distance-influence function is set up according to the distance from other nodes to the central node, and the feature extraction of information in the knowledge graph is completed according to the distance-influence function. Finally, the knowledge graph features are used as an auxiliary to complete the recommendation task. In the experiment, three data sets were selected to compare with other advanced models, and it was found that the model is in an advantageous position in terms of training time and recommendation effect, and has a certain degree of universality.

Graph convolutional network – Long short term memory neural network- multi layer perceptron- Gaussian progress regression model: A new deep learning model for predicting ozone concertation

The objective of extracting relations between document components lies in identifying the Within a single document, the focus often lies on understanding the connections between different entities. This type of analysis goes beyond individual sentences, as it demands an understanding of how information from multiple sentences interacts to form these connections. Over recent years, the importance of exploring relationships involving several entities simultaneously has grown significantly. To advance the field of studying such connections across entire documents, a novel collection of data points, known as DocRED, has been introduced. Currently, the standard approach for this task involves using BiLSTM networks to process the entire document as a whole. However, this method struggles to effectively capture the intricate relationships that exist among various entities. To overcome this limitation, a new model designed for document-level relationship identification has been developed, which leverages Graph Convolutional Networks (GCN). GCNs are particularly useful here because they can gather information from surrounding entities, allowing for a more detailed modeling of their interactions. The proposed approach starts by identifying coreferential links to gather features that represent the relationships between pairs of entities. These features are then analyzed using GCN to construct a graph structure that represents the entire document, ultimately revealing the complex interactions between different entities. Testing this model on the large-scale DocRED dataset from Tsinghua University has demonstrated its strong performance in this challenging task.