TF-DWGNet: a directed weighted graph neural network with tensor fusion for multi-omics cancer subtype classification

Abstract Integration and analysis of multi-omics data provide valuable insights for improving cancer subtype classification. However, such data are inherently heterogeneous, high-dimensional, and exhibit complex intra- and inter-modality dependencies. Graph neural networks provide a principled framework for modeling these structures, but existing approaches often rely on prior knowledge or predefined similarity networks that produce either undirected or unweighted graphs, failing to capture task-specific directionality and interaction strengths. Interpretability at both the modality and feature levels also remains limited. To address these challenges, we propose “TF-DWGNet,” a novel Graph Neural Network framework that combines tree-based Directed Weighted graph construction with Tensor Fusion for multiclass cancer subtype classification. TF-DWGNet introduces two key methodological innovations: (i) a supervised tree-based strategy that constructs directed weighted graphs tailored to each omics modality, and (ii) a tensor fusion mechanism that captures unimodal, bimodal, and trimodal interactions using low-rank decomposition for computational efficiency. Experiments on three real-world cancer datasets demonstrate that TF-DWGNet consistently outperforms state-of-the-art baselines across multiple metrics and statistical tests. In addition, the model provides biologically meaningful insights through modality level contribution scores and ranked feature importance. These results highlight that TF-DWGNet is an effective and interpretable solution for multi-omics integration in cancer research.