A Multi-Relational Graph Encoder Network for Fine-Grained Prediction of MiRNA-Disease Associations.

MicroRNAs (miRNAs) are critical in diagnosing and treating various diseases. Automatically demystifying the interdependent relationships between miRNAs and diseases has recently made remarkable progress, but their fine-grained interactive relationships still need to be explored. We propose a multi-relational graph encoder network for fine-grained prediction of miRNA-disease associations (MRFGMDA), which uses practical and current datasets to construct a multi-relational graph encoder network to predict disease-related miRNAs and their specific relationship types (upregulation, downregulation, or dysregulation).

Multi-View Graph Contrastive Learning via Adaptive Channel Optimization for Depression Detection in EEG Signals.

Automated detection of depression using Electroencephalogram (EEG) signals has become a promising application in advanced bioinformatics technology. Although current methods have achieved high detection performance, several challenges still need to be addressed: (1) Previous studies do not consider data redundancy when modeling multi-channel EEG signals, resulting in some unrecognized noise channels remaining. (2) Most works focus on the functional connection of EEG signals, ignoring their spatial proximity.

Dexamethasone combined metronidazole on mammary duct ectasia and its relationship with serum IL-10 and IL-17.

Aim: To explore the effect of dexamethasone combined with metronidazole in the treatment of mammary duct ectasia (MDE) and its relationship with changes in serum interleukin-10 (IL-10) and IL-17 expression.

Methods: One hundred and twenty patients with MDE were divided into two groups randomly, control and observation groups (each n = 60). Another 50 patients with normal physical examination were recruited in the normal group.