Hierarchical Negative Sampling Based Graph Contrastive Learning Approach for Drug-Disease Association Prediction

Information Science, Computer Science, Artificial Intelligence, Medicine, Pharmacy, Life Sciences, Biochemistry,
Drug-Disease Association   Contrastive Learning   Graph Neural Networks   Negative Sampling   Heterogeneous Network  

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...

Immunotherapy Efficacy Prediction for Non-Small Cell Lung Cancer Using Multi-View Adaptive Weighted Graph Convolutional Networks

Information Science, Computer Science, Artificial Intelligence, Medicine, Medical Imaging, Oncology, Life Sciences, Immunology,
immunotherapy   non-small cell lung cancer   graph convolutional networks   multi-view   radiomics  

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 ...

KG4NH: A Comprehensive Knowledge Graph for Question Answering in Dietary Nutrition and Human Health

Information Science, Computer Science, Artificial Intelligence, Medicine, Environmental and Public Health, Life Sciences, Bioengineering,
knowledge graph   text mining   question answering   nutrition   human diseases  

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...

Predicting Drug-Target Affinity by Learning Protein Knowledge from Biological Networks

Information Science, Computer Science, Artificial Intelligence, Life Sciences, Biochemistry, Bioengineering, Biophysics,
drug-target affinity   variational graph autoencoders   graph convolutional network   protein-protein interaction  

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...

Knowledge-Enhanced Graph Topic Transformer for Explainable Biomedical Text Summarization

Information Science, Computer Science, Artificial Intelligence, Medicine, Environmental and Public Health, Medical Laboratory Science, Life Sciences, Biochemistry,
biomedical text summarization   domain knowledge   graph neural network   explainability   pre-trained language models  

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...