TF-DWGNet: A Directed Weighted Graph Neural Network with Tensor Fusion for Multi-Omics Cancer Subtype Classification

• URL: http://arxiv.org/abs/2509.16301v1
• Date: Fri, 19 Sep 2025 17:52:25 GMT
• Title: TF-DWGNet: A Directed Weighted Graph Neural Network with Tensor Fusion for Multi-Omics Cancer Subtype Classification
• Authors: Tiantian Yang, Zhiqian Chen,
• Abstract summary: We propose TF-DWGNet, a graph neural network framework for multi-omics integration and analysis.<n>It combines tree-based Directed Weighted graph construction with tensor Fusion for multiclass cancer subtype classification.<n>Experiments show that TF-DWGNet consistently outperforms state-of-the-art baselines across multiple metrics and statistical tests.
• Score: 7.924798643791988
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Integration and analysis of multi-omics data provide valuable insights for cancer subtype classification. However, such data are inherently heterogeneous, high-dimensional, and exhibit complex intra- and inter-modality dependencies. Recent advances in graph neural networks (GNNs) offer powerful tools for modeling such structure. Yet, most existing methods rely on prior knowledge or predefined similarity networks to construct graphs, which are often undirected or unweighted, failing to capture the directionality and strength of biological interactions. 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 innovations: a supervised tree-based approach for constructing directed, weighted graphs tailored to each omics modality, and a tensor fusion mechanism that captures unimodal, bimodal, and trimodal interactions using low-rank decomposition for efficiency. TF-DWGNet enables modality-specific representation learning, joint embedding fusion, and interpretable subtype prediction. Experiments on real-world cancer datasets show that TF-DWGNet consistently outperforms state-of-the-art baselines across multiple metrics and statistical tests. Moreover, it provides biologically meaningful insights by ranking influential features and modalities. These results highlight TF-DWGNet's potential for effective and interpretable multi-omics integration in cancer research.

Related papers

• Multi-Level Fusion Graph Neural Network for Molecule Property Prediction [8.629821238312621]
  We propose a Multi-Level Fusion Graph Neural Network (MLFGNN) that integrates Graph Attention Networks and a novel Graph Transformer.<n>Experiments on multiple benchmark datasets demonstrate that MLFGNN consistently outperforms state-of-the-art methods in both classification and regression tasks.
  arXiv  Detail & Related papers  (2025-07-04T09:38:19Z)
• Heterogeneous network drug-target interaction prediction model based on graph wavelet transform and multi-level contrastive learning [8.154286666697312]
  This study proposes a heterogeneous network drug target interaction prediction framework.<n>It integrates graph neural network and multi scale signal processing technology to construct a model with both efficient prediction and multi level interpretability.<n> Experimental results show that our framework shows excellent prediction performance on all datasets.
  arXiv  Detail & Related papers  (2025-04-27T09:29:50Z)
• Tensor Convolutional Network for Higher-Order Interaction Prediction in Sparse Tensors [74.31355755781343]
  We propose TCN, an accurate and compatible tensor convolutional network that integrates seamlessly with TF methods for predicting top-k interactions.<n>We show that TCN integrated with a TF method outperforms competitors, including TF methods and a hyperedge prediction method.
  arXiv  Detail & Related papers  (2025-03-14T18:22:20Z)
• Overlap-aware meta-learning attention to enhance hypergraph neural networks for node classification [7.822666400307049]
  We propose a novel framework, overlap-aware meta-learning attention for hypergraph neural networks (OMA-HGNN)<n>First, we introduce a hypergraph attention mechanism that integrates both structural and feature similarities. Specifically, we linearly combine their respective losses with weighted factors for the HGNN model.<n>Second, we partition nodes into different tasks based on their diverse overlap levels and develop a multi-task Meta-Weight-Net (MWN) to determine the corresponding weighted factors.<n>Third, we jointly train the internal MWN model with the losses from the external HGNN model and train the external model with the weighted factors
  arXiv  Detail & Related papers  (2025-03-11T01:38:39Z)
• Comparative Analysis of Multi-Omics Integration Using Advanced Graph Neural Networks for Cancer Classification [40.45049709820343]
  Multi-omics data integration poses significant challenges due to the high dimensionality, data complexity, and distinct characteristics of various omics types. This study evaluates three graph neural network architectures for multi-omics (MO) integration based on graph-convolutional networks (GCN), graph-attention networks (GAT), and graph-transformer networks (GTN)
  arXiv  Detail & Related papers  (2024-10-05T16:17:44Z)
• IGCN: Integrative Graph Convolution Networks for patient level insights and biomarker discovery in multi-omics integration [2.0971479389679337]
  We introduce a novel integrative neural network approach for cancer molecular subtype and biomedical classification applications. IGCN can identify which types of omics receive more emphasis for each patient to predict a certain class. IGCN has the capability to pinpoint significant biomarkers from a range of omics data types.
  arXiv  Detail & Related papers  (2024-01-31T05:52:11Z)
• MGNNI: Multiscale Graph Neural Networks with Implicit Layers [53.75421430520501]
  implicit graph neural networks (GNNs) have been proposed to capture long-range dependencies in underlying graphs. We introduce and justify two weaknesses of implicit GNNs: the constrained expressiveness due to their limited effective range for capturing long-range dependencies, and their lack of ability to capture multiscale information on graphs at multiple resolutions. We propose a multiscale graph neural network with implicit layers (MGNNI) which is able to model multiscale structures on graphs and has an expanded effective range for capturing long-range dependencies.
  arXiv  Detail & Related papers  (2022-10-15T18:18:55Z)
• Relation Embedding based Graph Neural Networks for Handling Heterogeneous Graph [58.99478502486377]
  We propose a simple yet efficient framework to make the homogeneous GNNs have adequate ability to handle heterogeneous graphs. Specifically, we propose Relation Embedding based Graph Neural Networks (RE-GNNs), which employ only one parameter per relation to embed the importance of edge type relations and self-loop connections.
  arXiv  Detail & Related papers  (2022-09-23T05:24:18Z)
• Simple and Efficient Heterogeneous Graph Neural Network [55.56564522532328]
  Heterogeneous graph neural networks (HGNNs) have powerful capability to embed rich structural and semantic information of a heterogeneous graph into node representations. Existing HGNNs inherit many mechanisms from graph neural networks (GNNs) over homogeneous graphs, especially the attention mechanism and the multi-layer structure. This paper conducts an in-depth and detailed study of these mechanisms and proposes Simple and Efficient Heterogeneous Graph Neural Network (SeHGNN)
  arXiv  Detail & Related papers  (2022-07-06T10:01:46Z)
• Mixed Graph Contrastive Network for Semi-Supervised Node Classification [63.924129159538076]
  We propose a novel graph contrastive learning method, termed Mixed Graph Contrastive Network (MGCN)<n>In our method, we improve the discriminative capability of the latent embeddings by an unperturbed augmentation strategy and a correlation reduction mechanism.<n>By combining the two settings, we extract rich supervision information from both the abundant nodes and the rare yet valuable labeled nodes for discriminative representation learning.
  arXiv  Detail & Related papers  (2022-06-06T14:26:34Z)
• Attention-driven Graph Clustering Network [49.040136530379094]
  We propose a novel deep clustering method named Attention-driven Graph Clustering Network (AGCN) AGCN exploits a heterogeneous-wise fusion module to dynamically fuse the node attribute feature and the topological graph feature. AGCN can jointly perform feature learning and cluster assignment in an unsupervised fashion.
  arXiv  Detail & Related papers  (2021-08-12T02:30:38Z)
• Ensemble manifold based regularized multi-modal graph convolutional network for cognitive ability prediction [33.03449099154264]
  Multi-modal functional magnetic resonance imaging (fMRI) can be used to make predictions about individual behavioral and cognitive traits based on brain connectivity networks. We propose an interpretable multi-modal graph convolutional network (MGCN) model, incorporating the fMRI time series and the functional connectivity (FC) between each pair of brain regions. We validate our MGCN model on the Philadelphia Neurodevelopmental Cohort to predict individual wide range achievement test (WRAT) score.
  arXiv  Detail & Related papers  (2021-01-20T20:53:07Z)
• Amortized Probabilistic Detection of Communities in Graphs [39.56798207634738]
  We propose a simple framework for amortized community detection. We combine the expressive power of GNNs with recent methods for amortized clustering. We evaluate several models from our framework on synthetic and real datasets.
  arXiv  Detail & Related papers  (2020-10-29T16:18:48Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.