The increasing specialization and segmentation of modern medical practice, while improving expertise, pose significant challenges in efficiently connecting patients with the right healthcare professionals. The vast array of medical specializations, coupled with sparse data on doctor profiles, overwhelms traditional recommendation algorithms. This study introduces CLEAR-Med: A Contrastive Learning-Enhanced knowledge grAph Recommender designed to match patients with healthcare providers in specific Medical subfields. CLEAR-Med leverages a domain-specific Knowledge Graph (KG) and advanced Contrastive Learning (CL) techniques to capture the nuanced expertise and preferences of doctors, effectively addressing data sparsity and information overload in Online Healthcare Communities (OHCs). The system constructs a comprehensive KG enriched with diverse information, including doctors’ social relationships, professional networks, and specialized attributes derived from OHC data. By embedding key entities and attributes through CL, CLEAR-Med generates robust representations, supported by a flexible attribute encoding module that integrates both efficient LSTMs and powerful Transformer-based models. Its modular prediction layer, featuring options from a stable Multilayer Perceptron (MLP) to an advanced generative diffusion model, then produces highly accurate and personalized recommendation sequences. CLEAR-Med demonstrates superior recommendation performance in baseline comparison experiments, excelling in adaptability and accuracy within OHC settings. Ablation studies confirm the effectiveness of individual components, while further experiments exploring advanced architectures like Transformers and diffusion models highlight the strong balance our framework strikes between performance and computational efficiency. Beyond addressing data sparsity challenges, CLEAR-Med establishes a strong foundation for future advancements in specialized medical matching systems, filling critical research gaps in the domain.

Online Medical Consultations (OMCs) are increasingly prevalent in Online Healthcare Communities (OHCs), with a considerable focus on doctor recommendations. However, existing research has predominantly centered on recommending individual specialized doctors, neglecting the exploration of more reliable interdisciplinary options for collaborative consultations. This paper introduces an innovative framework for interdisciplinary doctor recommendations for collaborative work in OMC scenarios, comprising two modules: one for calculating expertise and another for collaborative computing. Leveraging the medical knowledge graph, the former employs an expertise encoder to derive specialty embeddings for patient queries, doctor profiles, and historical consultations. The latter builds a collaborative circle for each doctor based on three dimensions: scientific citation, academic collaboration, and spatiotemporal proximity. This circle analysis delves into the potential for cooperation between doctors, aiming to identify optimal combinations. Experimental results on two real datasets demonstrate the superior performance of our proposed model compared to state-of-the-art methods. Ablation studies highlight the significant contribution of the three social network dimensions and their interactions to collaborative computing outcomes. Our model exhibits robustness across different parameter settings, as demonstrated through experiments. The study's results empower practitioners to develop a portfolio of recommendations, enhancing the effectiveness of cooperative consultations for complex diseases requiring multidisciplinary collaboration.

MOTIVATION: Accurate inference of potential drug-protein interactions (DPIs) aids in understanding drug mechanisms and developing novel treatments. Existing deep learning models, however, struggle with accurate node representation in DPI prediction, limiting their performance. RESULTS: We propose a new computational framework that integrates global and local features of nodes in the drug-protein bipartite graph for efficient DPI inference. Initially, we employ pre-trained models to acquire fundamental knowledge of drugs and proteins and to determine their initial features. Subsequently, the MinHash and HyperLogLog algorithms are utilized to estimate the similarity and set cardinality between drug and protein subgraphs, serving as their local features. Then, an energy-constrained diffusion mechanism is integrated into the transformer architecture, capturing interdependencies between nodes in the drug-protein bipartite graph and extracting their global features. Finally, we fuse the local and global features of nodes and employ multilayer perceptrons to predict the likelihood of potential DPIs. A comprehensive and precise node representation guarantees efficient prediction of unknown DPIs by the model. Various experiments validate the accuracy and reliability of our model, with molecular docking results revealing its capability to identify potential DPIs not present in existing databases. This approach is expected to offer valuable insights for furthering drug repurposing and personalized medicine research. AVAILABILITY AND IMPLEMENTATION: Our code and data are accessible at: https://github.com/ZZCrazy00/DPI.

Accurately recognizing the type of complex electromagnetic jamming is the essential prerequisite for synthetic aperture radar (SAR) anti-jamming. However, current convolutional neural network (CNN)-based SAR jamming recognition methods require balanced training samples, which contradicts the varying difficulty of acquiring various jamming types, drastically reducing the recognition accuracy and generalization ability. This article proposes a discriminative feature distance metric model, JRSNet, for jamming recognition under imbalanced training samples, by refining the jamming modulation differences in the time-frequency (TF) domain into discriminative features. Novel feature discriminative distance metric (FD2M) loss function and discriminative feature constraint module (DFCM) are put forward to guarantee JRSNet learns embedding expression paradigm from jamming TF spectrograms to discriminative features, thus eliminating the influence of imbalanced training samples. Moreover, new spatial and channel attention modules are incorporated into JRSNet to capture jamming modulation information from multiple dimensions, consequently further improving recognition accuracy. Precisely because of the captured modulation regions in feature maps by spatial attention, the proposed approach can achieve jamming suppression synchronously. Experimental results show that under imbalanced training samples, JRSNet can accurately identify multiple jamming types both within and outside the training dataset with high generalizability. Compared with the existing jamming recognition methods, JRSNet performs superior recognition while taking into account good jamming suppression performance.

The complex interactions between multistage manufacturing processes and different processing technologies is difficult to express with unified mathematical expression. In this paper, the reliability robust design model was studied based on directed topological graph. This model takes the quality characteristic variation loss in the manufacturing process as the objective function, with process sequence as constraints, taking into account the manufacturing process tolerances and inherent reliability of the output products to solve the problem of process procedure optimization and process parameters optimization. Finally, this paper applied a case about servo valve manufacturing process to verify the validity of the proposed method.