ABSTRACT Self‐collision handling remains a fundamental and long‐standing challenge in neural cloth simulation, particularly for loose garments with complex topologies. We propose a self‐supervised neural cloth simulation framework that integrates Gaussian mixture skinning (GMS) with a differentiable collision‐overlap loss to significantly enhance physical plausibility and visual realism. We employ continuous and spatially smooth GMS weights to model vertex‐skeleton coupling, enabling stable deformations under large body motions. To explicitly address cloth self‐collisions, we introduce a differentiable spatial repulsion constraint that suppresses interpenetration and layer‐overlap artifacts. The proposed objective is jointly optimized with physics‐inspired losses, enabling the network to learn consistent cloth dynamics without relying on ground‐truth physical simulations. Experimental results demonstrate improved temporal stability, reduced collision artifacts, and stronger generalization compared to existing self‐supervised methods.

Leveraging the wealth of biomedical data available, we can derive insights into the relationships between biological entities from various angles. This underscores the complexity and significance of developing a dynamic approach for integrating data from multiple sources, a critical endeavor in drug recommendation. In this study, we introduce an innovative deep learning approach termed "Multi-View Fusion Strategy with Self-Attention" (FusionMVSA), designed to predict associations between drugs and diseases. To effectively amalgamate data from diverse sources and extract representative features, we have developed a feature extraction mechanism that capitalizes on similarities. This mechanism computes self-attention across multiple perspectives using shared group parameters, thereby highlighting common characteristics. Simultaneously, we utilize biomedical similarities among multi-source data as guiding factors for calculating similarity, enabling the capture of more nuanced features. Subsequently, we integrate these features through a feature fusion process, where known associations between drugs and diseases act as guiding terms. This strategy allows us to uncover the complementary aspects of different viewpoints. Ultimately, we predict potential drug-disease associations using a multi-layer perceptron neural network. Our methodology has undergone rigorous testing through various cross-validation experiments and case studies. We are confident that FusionMVSA will prove to be a valuable tool in drug recommendation, offering new avenues for exploration and discovery in the quest to combat diseases.

Predicting drug-target interactions (DTI) is critical for advancing drug discovery. However, existing DTI approaches struggle with data imbalance and heterogeneous information. This study presents a novel framework called PDGCL-DTI, which leverages two graph contrastive learning frameworks in parallel to capture both local and global features from drug-target heterogeneous networks. First, PDGCL-DTI effectively handles data imbalance through the AdaL-GCL module, which dynamically adjusts the weights of minority class samples to mitigate the impact of the imbalance. Second, by combining local and global contrastive learning, it extracts features from both local node information and global structural information, improving its adaptability to complex heterogeneous networks. This dual strategy enables PDGCL-DTI to exhibit greater robustness and higher prediction accuracy when handling complex DTI data. Experimental results on the ChEMBL, DrugBank, and DAVIS datasets show that PDGCL-DTI outperforms existing DTI methods, achieving an average AUC of 0.958 and an average accuracy of 0.95 across the three datasets. Additionally, case studies demonstrate that PDGCL-DTI successfully predicts interactions between Enasidenib and GABA-AT, as well as Sorafenib and Caspase-3, underscoring its practical applicability in the visualization workflow on the ChEMBL dataset.

Drug-Drug Interaction Prediction (DDI) is critical for ensuring safe medication. However, existing methods often rely on local structures or limited single-view molecular information, making them insufficient to capture long-range dependencies and multi-scale chemical semantics. To address these limitations, we propose MVCADF - a relation-aware Graph Transformer framework with multi-view consistency alignment and dynamic fusion for DDIs. Beyond leveraging molecular and biomedical knowledge graphs, MVCADF integrates motif fragment graphs, element-level view, and ECFP fingerprints, thus enabling comprehensive representations at the atom, fragment, element, fingerprint, and semantic levels. We design a multi-view consistency alignment strategy to project different views into a shared anchor space, which mitigates inter-view distributional shift and enhances representational coherence. This strategy is followed by a dynamic fusion module that adaptively integrates multi-view features based on their discriminative significance. Additionally, the relation- and distance-aware attention mechanism further facilitates modeling of long-range interactions within heterogeneous graphs. Extensive experiments show that MVCADF outperforms the best baseline by 3% - 5% across multiple public datasets, offering a robust and holistic solution for DDIs prediction.

Spatial transcriptomics (STs) technology is a powerful technique that simultaneously preserves gene expression profiles and spatial information, enabling deeper exploration of tissue organization and function. However, many existing computational approaches often rely on labeled ST data and overlook the rich spatial information, resulting in limited representations and suboptimal clustering. In this paper, we propose ST-GCP, a self-supervised graph representation learning framework for ST data, which incorporates a structure-feature perturbation mechanism. First, ST-GCP applies feature-level random permutation of the gene expression matrix and random edge dropout in the spatial neighbor network, creating two complementary augmented graph views of ST data. ST-GCP then employs a two-layer graph convolutional network (GCN) encoder-decoder to extract spatial representations and reconstruct gene expression. Finally, a cosine-similarity-based contrastive objective aligns the view-specific representations, and the overall loss jointly optimizes reconstruction fidelity and contrastive consistency, thereby coupling graph topology with transcriptomic profiles in a shared low-dimensional space. Experimental results on multiple ST datasets demonstrate that ST-GCP can uncover biologically meaningful patterns, such as tumor heterogeneity, brain developmental architecture, and cellular developmental trajectories.

MOTIVATION: The classification task based on whole-slide images (WSIs) is a classic problem in computational pathology. Multiple instance learning (MIL) provides a robust framework for analyzing whole slide images with slide-level labels at gigapixel resolution. However, existing MIL models typically focus on modeling the relationships between instances while neglecting the variability across the channel dimensions of instances, which prevents the model from fully capturing critical information in the channel dimension. RESULTS: To address this issue, we propose a plug-and-play module called Multi-scale Channel Attention Block (MCAB), which models the interdependencies between channels by leveraging local features with different receptive fields. By alternately stacking four layers of Transformer and MCAB, we designed a channel attention-based MIL model (CAMIL) capable of simultaneously modeling both inter-instance relationships and intra-channel dependencies. To verify the performance of the proposed CAMIL in classification tasks, several comprehensive experiments were conducted across three datasets: Camelyon16, TCGA-NSCLC, and TCGA-RCC. Empirical results demonstrate that, whether the feature extractor is pretrained on natural images or on WSIs, our CAMIL surpasses current state-of-the-art MIL models across multiple evaluation metrics. AVAILABILITY AND IMPLEMENTATION: All implementation code is available at https://github.com/maojy0914/CAMIL.

The process of new drug development is complex, whereas drug-disease association (DDA) prediction aims to identify new therapeutic uses for existing medications. However, existing graph contrastive learning approaches typically rely on single-view contrastive learning, which struggle to fully capture drug-disease relationships. Subsequently, we introduce a novel multi-view contrastive learning framework, named CDPMF-DDA, which enhances the model's ability to capture drug-disease associations by incorporating diverse information representations from different views. First, we decompose the original drug-disease association matrix into drug and disease feature matrices, which are then used to reconstruct the drug-disease association network, as well as the drug-drug and disease-disease similarity networks. This process effectively reduces noise in the data, establishing a reliable foundation for the networks produced. Next, we generate multiple contrastive views from both the original and generated networks. These views effectively capture hidden feature associations, significantly enhancing the model's ability to represent complex relationships. Extensive cross-validation experiments on three standard datasets show that CDPMF-DDA achieves an average AUC of 0.9475 and an AUPR of 0.5009, outperforming existing models. Additionally, case studies on Alzheimer's disease and epilepsy further validate the model's effectiveness, demonstrating its high accuracy and robustness in drug-disease association prediction. Based on a multi-view contrastive learning framework, CDPMF-DDA is capable of integrating multi-source information and effectively capturing complex drug-disease associations, making it a powerful tool for drug repositioning and the discovery of new therapeutic strategies.

In recent years, deep learning achieves significant advancements in medical image segmentation. Research finds that integrating Transformers and CNNs effectively addresses the limitations of CNNs in managing long-distance dependencies and understanding global information.However, existing models typically employ a serial approach to combine Transformers and CNNs, which complicates the simultaneous processing of global and local information. To address this, our study proposes a parallel multi-path attention architecture, SWMA-UNET, that integrates Transformers and CNNs. This architecture deeply mines features through parallel strategies while capturing both local details and global context information, thereby enhancing the accuracy of medical image segmentation. Experimental results indicate that our method surpasses all previously reported methods in the literature on the Synapse, ACDC, ISIC 2018 and MoNuSeg datasets.

Drug repositioning has emerged as a promising strategy for identifying new therapeutic applications for existing drugs. In this study, we present DRGBCN, a novel computational method that integrates heterogeneous information through a deep bilinear attention network to infer potential drugs for specific diseases. DRGBCN involves constructing a comprehensive drug-disease network by incorporating multiple similarity networks for drugs and diseases. Firstly, we introduce a layer attention mechanism to effectively learn the embeddings of graph convolutional layers from these networks. Subsequently, a bilinear attention network is constructed to capture pairwise local interactions between drugs and diseases. This combined approach enhances the accuracy and reliability of predictions. Finally, a multi-layer perceptron module is employed to evaluate potential drugs. Through extensive experiments on three publicly available datasets, DRGBCN demonstrates better performance over baseline methods in 10-fold cross-validation, achieving an average area under the receiver operating characteristic curve (AUROC) of 0.9399. Furthermore, case studies on bladder cancer and acute lymphoblastic leukemia confirm the practical application of DRGBCN in real-world drug repositioning scenarios. Importantly, our experimental results from the drug-disease network analysis reveal the successful clustering of similar drugs within the same community, providing valuable insights into drug-disease interactions. In conclusion, DRGBCN holds significant promise for uncovering new therapeutic applications of existing drugs, thereby contributing to the advancement of precision medicine.

Drug repositioning, the strategy of redirecting existing drugs to new therapeutic purposes, is pivotal in accelerating drug discovery. While many studies have engaged in modeling complex drug-disease associations, they often overlook the relevance between different node embeddings. Consequently, we propose a novel weighted local information augmented graph neural network model, termed DRAGNN, for drug repositioning. Specifically, DRAGNN firstly incorporates a graph attention mechanism to dynamically allocate attention coefficients to drug and disease heterogeneous nodes, enhancing the effectiveness of target node information collection. To prevent excessive embedding of information in a limited vector space, we omit self-node information aggregation, thereby emphasizing valuable heterogeneous and homogeneous information. Additionally, average pooling in neighbor information aggregation is introduced to enhance local information while maintaining simplicity. A multi-layer perceptron is then employed to generate the final association predictions. The model's effectiveness for drug repositioning is supported by a 10-times 10-fold cross-validation on three benchmark datasets. Further validation is provided through analysis of the predicted associations using multiple authoritative data sources, molecular docking experiments and drug-disease network analysis, laying a solid foundation for future drug discovery.