The all-weather imaging capability of synthetic aperture radar (SAR) confers unique advantages for maritime surveillance. However, ship detection under complex sea conditions still faces challenges, such as high-frequency noise interference and the limited computational power of edge computing platforms. To address these challenges, we propose a lightweight SAR small ship detection network, LWSARDet, which mitigates feature redundancy and reduces computational complexity in existing models. Specifically, based on the YOLOv5 framework, a dual strategy for the lightweight network is adopted as follows: On the one hand, to address the limited nonlinear representation ability of the original network, a global channel attention mechanism is embedded and a feature extraction module, GCCR-GhostNet, is constructed, which can effectively enhance the network’s feature extraction capability and high-frequency noise suppression, while reducing computational cost. On the other hand, to reduce feature dilution and computational redundancy in traditional detection heads when focusing on small targets, we replace conventional convolutions with simple linear transformations and design a lightweight detection head, LSD-Head. Furthermore, we propose a Position–Morphology Matching IoU loss function, P-MIoU, which integrates center distance constraints and morphological penalty mechanisms to more precisely capture the spatial and structural differences between predicted and ground truth bounding boxes. Extensive experiments conduct on the High-Resolution SAR Image Dataset (HRSID) and the SAR Ship Detection Dataset (SSDD) demonstrate that LWSARDet achieves superior overall performance compared to existing state-of-the-art (SOTA) methods.

Predicting Herb–Disease Associations (HDA) is pivotal for modernizing Traditional Chinese Medicine (TCM); however, this is impeded by data heterogeneity and the complex, multi-component mechanisms of herbal medicines. Existing drug–disease prediction models often struggle to capture high-order structural patterns and resolve semantic inconsistencies intrinsic to herbs. To overcome these limitations, we present HData, a standardized benchmark dataset that integrates herbal medicinal properties, chemical compositions, and disease associations. We further propose GHF-ACL, a novel multi-order graph contrastive learning framework designed for HDA prediction. Specifically, GHF-ACL explicitly models low-order functional similarities via a herb–disease similarity graph while capturing high-order component interactions through a herb–chemical hypergraph. Furthermore, an adaptive gating-guided structural interaction module aligns heterogeneous graph representations into a unified latent space, and hierarchical contrastive learning enforces consistency across structural views. Extensive experiments on five datasets demonstrate that GHF-ACL achieves superior or competitive performance over six state-of-the-art models across most metrics, with significant improvements over the best-performing baseline model in AUPR (+4.8% on LRSSL, + 3.81% on Cdata), F1 score, and Recall. These results underscore the model’s superior capability in detecting true positive associations within imbalanced biomedical data. By synergizing multi-view graph modeling, semantic fusion, and contrastive regularization, this work establishes a unified framework for HDA prediction, offering valuable insights for computational TCM and data-driven drug discovery.

Protein function prediction plays an important role in the field of biology. With the wide application of deep learning in the field of bioinformatics, more and more natural language processing (NLP) technologies are applied to the downstream tasks in the field of bioinformatics, and it has also shown excellent performance in protein function prediction. Protein-protein interaction (PPI) networks and other biological attributes contain rich information critical for annotating protein functions. However, existing deep learning networks still suffer from overfitting and noise issues, resulting in low accuracy in protein function prediction. Consequently, developing efficient models for protein function prediction remains a popular and challenging topic in the application of NLP in bioinformatics. In this study, we propose a novel protein function prediction model based on attention mechanisms, termed IMAGO. This model employs the Transformer pre-training process, integrating multi-head attention mechanisms and regularization techniques, and optimizes the loss function to effectively reduce overfitting and noise issues during training. It generates more robust embeddings, ultimately improving the accuracy of protein function prediction. Experimental results on human and mouse datasets indicate that our model surpasses other protein function prediction models across multiple metrics. Thus, this efficient, stable, and accurate deep learning model holds significant promise for protein function prediction.