Research on Drug-Disease Association Prediction Using Graph Contrastive Learning Based on Layered Negative Sampling The prediction of drug-disease associations (RDAs) plays a critical role in unveiling disease treatment strategies and promoting drug repurposing. However, existing methods mainly rely on limited domain-specific knowledge when predict...
Research Report on Immunotherapy Efficacy Prediction for Non-Small Cell Lung Cancer: A Study of Multi-View Adaptive Weighted Graph Convolutional Networks Background Introduction Lung cancer is a highly prevalent and poorly prognostic malignant tumor with a persistently high mortality rate. Among all lung cancer patients, Non-Small Cell Lung Cancer ...
Background and Research Motivation It is well-known that food nutrition is closely related to human health. Scientific research has shown that improper dietary nutrition is linked to more than 200 diseases. Especially when considering the metabolic processes of gut microbiota, the complex interactions between food nutrients and diseases become diff...
CIGNN: A Framework for Cuffless Continuous Blood Pressure Estimation Based on Causality and Graph Neural Networks Background Introduction According to data from the World Health Organization (WHO), approximately 1.13 billion people globally are affected by hypertension, and this number is expected to increase to 1.5 billion by 2025. Hypertension is...
Predicting Future Diseases: Integration of Temporal Knowledge Graphs and Medical Ontologies Electronic Health Records (EHRs) are indispensable tools in modern medical institutions. They record detailed health histories of patients, including demographics, medications, lab results, and treatment plans. This data not only improves the coordination an...
EHR-HGCN: A Novel Hybrid Heterogeneous Graph Convolutional Network Method for Electronic Health Record Text Classification Academic Background With the rapid development of Natural Language Processing (NLP), text classification has become an important research direction in this field. Text classification not only helps us understand the knowledge b...
Research Report on the Integration of Medical Relation Extraction and Knowledge Graph-Based Recommendations Background Introduction In the medical field, the explosive growth of literature makes it challenging for researchers to keep up with the latest advancements in their specific areas. From the perspective of natural language processing (NLP), ...
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-...