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...

GCTNet: A Graph Convolutional Transformer Network for Major Depressive Disorder Detection Based on EEG Signals

Engineering, Electrical Engineering, Information Science, Computer Science, Artificial Intelligence, Medicine, Psychiatry, Neurology, Clinical Psychology, Medical Imaging,
Major Depressive Disorder   EEG   Deep Learning   Phase Locking Value   Contrastive Learning  

GCTNet: Graph Convolution Transformer Network for Detecting Major Depressive Disorder Based on EEG Signals Research Background Major Depressive Disorder (MDD) is a prevalent mental illness characterized by significant and persistent low mood, affecting over 350 million people worldwide. MDD is one of the leading causes of suicide, resulting in appr...

Negative Deterministic Information-Based Multiple Instance Learning for Weakly Supervised Object Detection and Segmentation

Negative Deterministic Information-Based Multiple Instance Learning for Weakly Supervised Object Detection and Segmentation

Information Science, Artificial Intelligence,
Negative Deterministic Information   Multiple Instance Learning   Weakly Supervised Object Detection   Weakly Supervised Semantic Segmentation   Contrastive Learning  

Negative Deterministic Information-Based Multiple Instance Learning for Weakly Supervised Object Detection and Segmentation Background Introduction In the past decade, significant progress has been made in the field of computer vision, particularly in object detection and semantic segmentation. However, most of the designed algorithms and models he...