Drug repositioning is a promising approach to discover new therapeutic applications with existing drugs. However, existing Graph Neural Networks (GNNs)-based methods suffer from two limitations: (1) the sparsity of labeled drug-disease associations and high-quality node representations, despite the continuous emergence of biomedical knowledge from multiple modalities; and (2) the over-smoothing issue in GNNs, which causes representations to become indistinguishable, thereby degrading model performance. To address these issues, we propose Panacea, a graph learning framework that leverages multimodal information to enhance drug repositioning performance. Panacea introduces an automated knowledge acquisition and refinement pipeline that collects and encodes multimodal information, including drug molecular structures (SMILES strings), clinical symptom descriptions of diseases, and both homogeneous and heterogeneous biomedical graphs. The resulting multimodal embeddings are aligned via a learnable projection layer, providing strong initial node representations for graph learning. In the subsequent hierarchical graph learning stage, we improve the Graph Isomorphism Network (GIN) with gated mechanisms and residual connections to enhance the network's representation capacity and avoid over-smoothing. The multimodal embeddings and the graph learning module are jointly optimized in an end-toend manner, enabling node representations to fuse graph structure. Experimental results demonstrate that Panacea outperforms state-of-the-art methods, achieving significant improvements in drug repositioning tasks, especially in scenarios with sparse data and insufficient information representation. Code is available at here.

Early screening for prenatal depression is of critical importance to maternal and infant health. However, traditional screening methods are fundamentally limited by a confluence of challenges, including data imbalance, resource constraints, poor model adaptability, and a lack of interpretability. While Large Language Models (LLMs) show potential in medical text analysis, their direct application to class-imbalanced tasks requires significant optimization. To address these limitations, we propose and evaluate an Instruction Fine-Tuning methodology, integrating prompt engineering directly into the training phase by prepending each questionnaire-based case description with a structured, task-specific directive. This directive encodes expert domain knowledge and an explicit instruction for the model to prioritize high recall, thereby directly addressing the clinical need to minimize false negatives. Several language models were adapted using this instruction-augmented dataset via the parameter-efficient Low-Rank Adaptation (LoRA) technique. The experimental results demonstrate that models trained with our Instruction Fine-Tuning method exhibit significantly higher recall for the minority class and achieve a more balanced overall performance. Our best-performing instruction-tuned model achieved an AUC of 0.81 and a Balanced Accuracy of 0.72, validating the effectiveness of our approach. Crucially, our interpretability analysis revealed that the models learned to focus on clinically-relevant risk factors, providing transparent and plausible justifications for their predictions. This research confirms that Instruction Fine-Tuning is a highly effective methodology for adapting LLMs to sensitive, imbalanced clinical screening tasks. The proposed approach presents a robust, interpretable, and adaptable solution well-suited to the demands of low-resource medical environments.

Drug-Drug Interaction (DDI) task plays a crucial role in clinical treatment and drug development. Recently, deep learning methods have been successfully applied for DDI prediction. However, training deep learning models always need large amount of data, while known DDIs are scarce. To address this challenge, a graph neural network-based DDI prediction model named PNESR-DDI is proposed, which compensates for the lack of DDIs by enriching drug representations. First, to obtain initial node representations that incorporate rich semantic information from the biomedical knowledge graph (KG), a link prediction pre-training method on external KG is proposed in the node embedding pre-training module. Then, considering the large scale of the KG, subgraph extraction for the target drug pairs is introduced to reduce noise and decrease computational complexity in the subgraph anchoring module. After that, the subgraph is updated, and node similarities are propagated in the subgraph reconstruction module. Based on the node similarity scores, the subgraph is pruned and reconstructed, which adjusts node representations to be more conducive to DDI prediction. Finally, the drug embeddings, subgraph representations, and drug fingerprint features are concatenated to predict DDIs. PNESRDDI is evaluated on two benchmark DDI datasets: DrugBank and TWOSIDES. Experiment results show that PNESR-DDI achieves better performance than baselines. Ablation results validate the effectiveness of the pre-training method and the adaptive subgraph reconstruction strategy.

The Traditional Chinese Medicine’s ancient literature recorded the massive medical theories and abundant medical experiences. To better understand and utilize, the knowledge from the literature, the Acupuncture and Tuina Knowledge Graph is proposed in this paper. Meanwhile, a deep learning network is established for acupuncture and tuina-related entity recognition and entity-relationship extraction. Finally, the trained network is able to reach an 82%+ F1-score for NER and 70%+ F1-score for relationship extraction.

Particle swarm optimizations (PSOs) are drawing extensive attention from both research and engineering fields due to their simplicity and powerful global search ability. However, there are two issues needing to be improved: one is that the classical PSO converges slowly; the other is that classical PSO tends to result in premature convergence, especially for multi-modal problems. This paper attempts to address these two issues. Firstly, to improve the convergent efficiency, this paper proposes an asymptotic predicting model of the globally-optimal solution, which is used to predict the global optimum based on extracting the features reflecting the evolutionary trend. The predicted global optimum is then taken as the third exemplar, in a way similar to the individual historical best solution and the swarm historical best solution in guiding the evolutionary process of other particles. To reduce the probability that the population is trapped into a local optimum due to the premature phenomenon, this paper proposes an adaptive mutation strategy, which is used to help the trapped particles to escape away from the local optimum by using the extended non-uniform mutation operator. Finally, we combine the two entities to develop a globally-optimal prediction-based adaptive mutation particle swarm optimization (GPAM-PSO). In numerical experimental parts, we compare the proposed GPAM-PSO with 11 existing PSO variants by using 22 benchmark problems of 30-dimensions and 100-dimensions, respectively. Numerical experiments demonstrate that the proposed GPAM-PSO could improve the accuracy and efficiency remarkably, which means that the combination of the globally-optimal prediction-based search and the adaptive mutation strategy could accelerate the convergence and reduce premature phenomenon effectively. Generally speaking, GPAM-PSO performs most efficiently and robustly. Moreover, the performance on an engineering problem demonstrates the practical application of the proposed GPAM-PSO algorithm.