Self-Supervised Contrastive Learning on Attribute and Topology Graphs for Predicting Relationships Among lncRNAs, miRNAs and Diseases.

PCG-CAM: Enhanced class activation map using principal components of gradients and its applications in brain MRI.

HATZFS predicts pancreatic cancer driver biomarkers by hierarchical reinforcement learning and zero-forcing set.

MMGAT: a graph attention network framework for ATAC-seq motifs finding.

Graph pooling in graph neural networks: methods and their applications in omics studies.

Multi-view learning framework for predicting unknown types of cancer markers via directed graph neural networks fitting regulatory networks.

Feature Correspondences Increase and Hybrid Terms Optimization Warp for Image Stitching.

Leveraging multidimensional features for policy opinion sentiment prediction.