Research on Medical Package Recommendation Systems: Heterogeneous Graph Neural Network Based on Dual-Level Interaction Awareness With the widespread application of electronic health records (EHRs) in the medical field, how to mine potential and valuable medical knowledge to support clinical decision-making has become an important research direction...

Predicting Drug-Target Affinity Based on Learning Protein Knowledge from Biological Networks Background The prediction of drug-target affinity (DTA) plays a crucial role in drug discovery. Efficient and accurate DTA prediction can significantly reduce the time and economic costs of new drug development. In recent years, the explosive development of...

An Explainable and Personalized Cognitive Reasoning Model Based on Knowledge Graph: Toward Decision Making for General Practice Background General medicine, as an important part of community and family healthcare, covers different ages, genders, organ systems, and various diseases. Its core concept is human-centered, family-based, emphasizing long-...

Application of Knowledge-Enhanced Graph Topic Transformer in Interpretable Biomedical Text Summarization Research Background Due to the continuous increase in the volume of biomedical literature, the task of automatic biomedical text summarization has become increasingly important. In 2021 alone, 1,767,637 articles were published in the PubMed data...

Multiple Prior Knowledge Graph Neural Network in Multi-Omics Data Analysis Background Introduction Precision medicine is an important field for the future of healthcare as it provides personalized treatment plans for patients, improving treatment outcomes and reducing costs. For instance, due to the complex clinical, pathological, and molecular cha...

Application of Hierarchical Attentive Relational Network in Diagnostic Prediction In recent years, Electronic Health Records (EHR) have become extremely valuable in improving medical decision-making, online disease detection, and monitoring. At the same time, deep learning methods have also achieved great success in utilizing EHR for health risk pr...

Temporal Aggregation and Propagation Graph Neural Networks (TAP-GNN) Background Introduction A temporal graph is a graph structure with dynamic interactions between nodes over continuous time, where the topology evolves over time. Such dynamic changes enable nodes to exhibit varying preferences at different times, which is critical for capturing us...

AutoAlign: A Fully Automated and Efficient Knowledge Graph Alignment Method Driven by Large Language Models Knowledge Graphs (KG) have been widely applied in fields such as question-answering systems, dialogue systems, and recommendation systems. However, different Knowledge Graphs often store the same real-world entities in various forms, leading ...

Knowledge Graph-Based Socially Enhanced Explainable Recommendation System Introduction With the increasing amount of information in the Internet world, the relevant information about users and products has rapidly expanded, leading to a growing problem of information overload. Recommendation systems can effectively alleviate this problem by recomme...

Knowledge Enhanced Graph Neural Networks for Explainable Recommendation Introduction With the explosive growth of online information, recommendation systems play an essential role in solving the problem of information overload. Traditional recommendation systems typically rely on Collaborative Filtering (CF) methods, which generate recommendations ...