3 papers
q-bio.QM2026
TF-DWGNet: A Directed Weighted Graph Neural Network with Tensor Fusion for Multi-Omics Cancer Subtype Classification
Tiantian Yang, Zhiqian Chen
Integration and analysis of multi-omics data provide valuable insights for improving cancer subtype classification. However, such data are inherently heterogeneous, high-dimensiona…
cs.LG2026
MOTGNN: Interpretable Graph Neural Networks for Multi-Omics Disease Classification
Tiantian Yang, Zhiqian Chen
Integrating multi-omics data, such as DNA methylation, mRNA expression, and microRNA (miRNA) expression, offers a comprehensive view of the biological mechanisms underlying disease…
cs.LG2026
engGNN: A Dual-Graph Neural Network for Omics-Based Disease Classification and Feature Selection
Tiantian Yang, Yuxuan Wang, Zhenwei Zhou +1
Omics data, such as transcriptomics, proteomics, and metabolomics, provide critical insights into disease mechanisms and clinical outcomes. However, their high dimensionality, smal…