  The increase in high-dimensional multiomics data demands advanced integration
models to capture the complexity of human diseases. Graph-based deep learning
integration models, despite their promise, struggle with small patient cohorts
and high-dimensional features, often applying independent feature selection
without modeling relationships among omics. Furthermore, conventional
graph-based omics models focus on homogeneous graphs, lacking multiple types of
nodes and edges to capture diverse structures. We introduce a Heterogeneous
Graph ATtention network for omics integration (HeteroGATomics) to improve
cancer diagnosis. HeteroGATomics performs joint feature selection through a
multi-agent system, creating dedicated networks of feature and patient
similarity for each omic modality. These networks are then combined into one
heterogeneous graph for learning holistic omic-specific representations and
integrating predictions across modalities. Experiments on three cancer
multiomics datasets demonstrate HeteroGATomics' superior performance in cancer
diagnosis. Moreover, HeteroGATomics enhances interpretability by identifying
important biomarkers contributing to the diagnosis outcomes.
