Graph Neural Network for Representation Learning of Lung Cancer

Graph Neural Network for Representation Learning of Lung Cancer

Information Science, Computer Science, Artificial Intelligence, Medicine, Respiratory System, Medical Imaging, Oncology,
multiple instance learning   graph   whole slide images   graph neural networks   lung cancer   variational autoencoder  

Representation Learning of Lung Cancer Based on Graph Neural Networks Background Introduction With the rapid development of digital pathology, image-based diagnostic systems are becoming increasingly important in precise pathological diagnosis. These systems rely on Multiple Instance Learning (MIL) technology for Whole Slide Images (WSIs). However,...

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...

CIGNN: A Causality-Informed and Graph Neural Network Based Framework for Cuffless Continuous Blood Pressure Estimation

Information Science, Computer Science, Artificial Intelligence, Medicine, Medical Imaging, Cardiovascular System,
cuffless blood pressure measurement   causality   ECG   pulse transit time   graph neural network   cardiac signals  

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...

EHR-HGCN: An Enhanced Hybrid Approach for Text Classification Using Heterogeneous Graph Convolutional Networks in Electronic Health Records

EHR-HGCN: An Enhanced Hybrid Approach for Text Classification Using Heterogeneous Graph Convolutional Networks in Electronic Health Records

Information Science, Computer Science, Artificial Intelligence,
Electronic Health Records   Heterogeneous Graph Convolutional Networks   Text Classification   Natural Language Processing   Deep Learning  

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...