Increasing research suggests that microRNAs (miRNAs) serve an essential function as biomarkers in various diseases. The variations in miRNA expression can influence their corresponding mRNAs, which, in turn, regulate the expression of target genes. Recently, graph neural networks (GNNs) have been widely utilized to predict miRNA-disease associations. However, a single GNN model is insufficient for fully learning node representations. Furthermore, individual aggregation methods struggle to effectively extract diverse structural information and node weights. To address these challenges, we propose a method that incorporates Principal Neighborhood Aggregation (PNA) and Graph Attention Networks (GAT) for miRNA-disease association prediction. First, we integrated multiple datasets to construct a weighted heterogeneous graph that models miRNA-LncRNA-disease interactions. Subsequently, PNA extracted node representations using multiple aggregators simultaneously. Additionally, features derived from both PNA and GAT were fused using an attention mechanism. These combined representations were then fed into a fully connected neural network for prediction. Experimental results demonstrate that PNAGMDA achieves exceptional performance, with AUC values of 93.82% and 92.77% on HMDD v2.0 and v3.2, respectively. Case studies, along with supplementary findings, confirm PNAGMDA's reliability for miRNA-disease prediction.

Due to the complementarity of visible and infrared images, it has become more favorable to fuse these two modalities to improve the object detection accuracy in the remote sensing area. However, there are still some problems to be solved. Most of the existing algorithms focus too much on the local information and ignore long-range information when performing feature extraction on different modalities. Besides, coarse weighted fusion strategies do not fully utilize the information from different modalities, and the fusion structure ignores the importance of intermodal information exchange. To tackle these problems, a cross-modal feature fusion and interaction strategy for the convolutional neural network (CNN)-transformer-based object detection in visual and infrared remote sensing imagery is proposed. We adopt a parallel structure to extract the features of different modalities, separately. In visual and infrared modality, the convolutional layers and transformer encoders are cascaded to fully extract both local and long-range information. The cross-modal feature fusion and interaction module (CFFIM) adopts the attention mechanisms to jointly fuse different modal features at the same scale to improve the diversity of fused features, and the feature interaction enables the sharing of visible and infrared information. Experiments on the VEDAI dataset have demonstrated the effectiveness of the proposed scheme compared to other state-of-the-art algorithms.

Most deep learning-based hyperspectral pansharpening methods use the hyperspectral images (HSIs) as the ground truth. Training samples are usually obtained by blurring and downsampling the panchromatic image and HSI. However, the blurring and downsampling operation lose much spatial and spectral information. As a result, the model parameters trained by these reduced-resolution samples are unsuitable for fusing full-resolution images. To tackle this problem, we propose an unsupervised hyperspectral pansharpening method via ratio estimation (RE) and residual attention network (RE-RANet). The spatial and spectral information of the fused image are derived from the original panchromatic and HSI rather than reduced-resolution images. At first, we generate the initial ratio image using the ratio enhancement method. The initial ratio image is fine-tuned by the residual attention network (RANet) to generate a multichannel ratio image. Then, we inject the multichannel ratio image that contains spatial detail information into the HSI. Finally, the generated hyperspectral image is constrained by the spatial constraint loss and the spectral constraint loss. Experiments on the EO-1 and Chikusei datasets verify the effectiveness of the proposed method. Compared with other state-of-the-art approaches, our method performs well in qualitative visual effects and quantitative evaluation indicators.

Image classification plays a fundamental role in hyperspectral image (HSI) analysis. Since the mixed pixels of the urban areas are generally more complex than other areas, the following two problems remain to be considered while dealing with urban HSI classification: 1) due to the fact that the spectral feature of different mixed pixels varies greatly in the same class, HSI classification of urban area is susceptible to the representativeness of the training samples and 2) since the urban area is densely packed with objects of different size, the comprehensive use of the spatial and spectral features to classify the objects is a difficult problem. To tackle these problems, HSI classification based on multiscale spectral–spatial deformable network (S2-DNet) is proposed. First, a$k$-means clustering method is adopted to cluster spectrum of each class, and representative samples are selected from the spectrum after clustering to reduce the impact of intraclass variation. Second, a spectral–spatial joint network is designed to extract the low-level features, including spectral features and spatial features. Third, the deformable network is introduced to extract high-level features of the object. Experimental results demonstrated that the proposed method outperformed the state-of-the-art methods on two widely used HSI data sets.

It is observed that the great spectral variation in the same hyperspectral image (HSI) pixel class often leads to misclassification. To solve this problem, we have proposed an automatic clustering-based two-branch convolutional neural network (CNN): first, to reduce the intraclass spectral variation, the HSI pixels are automatically subdivided into smaller classes by clustering; second, in order to suppress the interference of spectral amplitude variation, the SincNet is introduced to capture the spectral pattern by giving more weight to the spectral shape; third, the DS-CNN with double directional strip convolution kernel is designed to extract spatial feature, so that specific contextual interactional features can be collected, especially in strip-shaped field-like roads and farmlands; finally, the spectral and spatial features extracted by the two branches are fused at fully connected layer to obtain an accurate classification. Extensive experiments demonstrated that the proposed method can obtain better classification performance than the state-of-the-art methods.