Soft-Evidence Fused Graph Neural Network for Cancer Driver Gene Identification across Multi-View Biological Graphs

• URL: http://arxiv.org/abs/2510.06290v1
• Date: Tue, 07 Oct 2025 05:20:57 GMT
• Title: Soft-Evidence Fused Graph Neural Network for Cancer Driver Gene Identification across Multi-View Biological Graphs
• Authors: Bang Chen, Lijun Guo, Houli Fan, Wentao He, Rong Zhang,
• Abstract summary: Identifying cancer driver genes (CDGs) is essential for understanding cancer mechanisms and developing targeted therapies.<n>Graph neural networks (GNNs) have recently been employed to identify CDGs by capturing patterns in biological interaction networks.<n>We propose Soft-Evidence Fusion Graph Neural Network (SEFGNN), a novel framework for CDG identification across multiple networks at the decision level.
• Score: 8.230375557003832
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Identifying cancer driver genes (CDGs) is essential for understanding cancer mechanisms and developing targeted therapies. Graph neural networks (GNNs) have recently been employed to identify CDGs by capturing patterns in biological interaction networks. However, most GNN-based approaches rely on a single protein-protein interaction (PPI) network, ignoring complementary information from other biological networks. Some studies integrate multiple networks by aligning features with consistency constraints to learn unified gene representations for CDG identification. However, such representation-level fusion often assumes congruent gene relationships across networks, which may overlook network heterogeneity and introduce conflicting information. To address this, we propose Soft-Evidence Fusion Graph Neural Network (SEFGNN), a novel framework for CDG identification across multiple networks at the decision level. Instead of enforcing feature-level consistency, SEFGNN treats each biological network as an independent evidence source and performs uncertainty-aware fusion at the decision level using Dempster-Shafer Theory (DST). To alleviate the risk of overconfidence from DST, we further introduce a Soft Evidence Smoothing (SES) module that improves ranking stability while preserving discriminative performance. Experiments on three cancer datasets show that SEFGNN consistently outperforms state-of-the-art baselines and exhibits strong potential in discovering novel CDGs.

Related papers

• Graph Neural Networks in Multi-Omics Cancer Research: A Structured Survey [1.0128808054306186]
  Data integration for multi-omics data has emerged as a powerful strategy to unravel the biological underpinnings of cancer.<n>Recent advancements in graph neural networks (GNNs) offer an effective framework to model heterogeneous and structured omics data.
  arXiv  Detail & Related papers  (2025-06-03T07:28:02Z)
• A Novel Graph Transformer Framework for Gene Regulatory Network Inference [0.27624021966289597]
  Inference of gene regulatory networks (GRNs) may not always reflect true biological interactions.<n>Most GRN inference methods face several challenges in the network reconstruction phase.<n>We employ autoencoder embeddings to capture gene expression patterns directly from raw data.<n>We embed the prior knowledge from GRN structures transforming them into a text-like representation.
  arXiv  Detail & Related papers  (2025-04-23T06:24:26Z)
• Analysis of Gene Regulatory Networks from Gene Expression Using Graph Neural Networks [0.4369058206183195]
  This study explores the use of Graph Neural Networks (GNNs), a powerful approach for modeling graph-structured data like Gene Regulatory Networks (GRNs) The model's adeptness in accurately predicting regulatory interactions and pinpointing key regulators is attributed to advanced attention mechanisms. The integration of GNNs in GRN research is set to pioneer developments in personalized medicine, drug discovery, and our grasp of biological systems.
  arXiv  Detail & Related papers  (2024-09-20T17:16:14Z)
• Domain-adaptive Message Passing Graph Neural Network [67.35534058138387]
  Cross-network node classification (CNNC) aims to classify nodes in a label-deficient target network by transferring the knowledge from a source network with abundant labels. We propose a domain-adaptive message passing graph neural network (DM-GNN), which integrates graph neural network (GNN) with conditional adversarial domain adaptation.
  arXiv  Detail & Related papers  (2023-08-31T05:26:08Z)
• Explainable Multilayer Graph Neural Network for Cancer Gene Prediction [21.83218536069088]
  We introduce an Explainable Multilayer Graph Neural Network (EMGNN) approach to identify cancer genes. Unlike conventional graph learning on a single biological network, EMGNN uses a multilayered graph neural network to learn from multiple biological networks for accurate cancer gene prediction. Our method consistently outperforms all existing methods, with an average 7.15% improvement in area under the precision-recall curve (AUPR) over the current state-of-the-art method.
  arXiv  Detail & Related papers  (2023-01-20T23:57:12Z)
• Unsupervised ensemble-based phenotyping helps enhance the discoverability of genes related to heart morphology [57.25098075813054]
  We propose a new framework for gene discovery entitled Un Phenotype Ensembles. It builds a redundant yet highly expressive representation by pooling a set of phenotypes learned in an unsupervised manner. These phenotypes are then analyzed via (GWAS), retaining only highly confident and stable associations.
  arXiv  Detail & Related papers  (2023-01-07T18:36:44Z)
• 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)
• Heterogeneous Graph based Deep Learning for Biomedical Network Link Prediction [7.628651624423363]
  We propose a Graph Pair based Link Prediction model (GPLP) for predicting biomedical network links. InP, 1-hop subgraphs extracted from known network interaction matrix is learnt to predict missing links. Our method demonstrates the potential applications in other biomedical networks.
  arXiv  Detail & Related papers  (2021-01-28T07:35:29Z)
• G-MIND: An End-to-End Multimodal Imaging-Genetics Framework for Biomarker Identification and Disease Classification [49.53651166356737]
  We propose a novel deep neural network architecture to integrate imaging and genetics data, as guided by diagnosis, that provides interpretable biomarkers. We have evaluated our model on a population study of schizophrenia that includes two functional MRI (fMRI) paradigms and Single Nucleotide Polymorphism (SNP) data.
  arXiv  Detail & Related papers  (2021-01-27T19:28:04Z)
• AM-GCN: Adaptive Multi-channel Graph Convolutional Networks [85.0332394224503]
  We study whether Graph Convolutional Networks (GCNs) can optimally integrate node features and topological structures in a complex graph with rich information. We propose an adaptive multi-channel graph convolutional networks for semi-supervised classification (AM-GCN) Our experiments show that AM-GCN extracts the most correlated information from both node features and topological structures substantially.
  arXiv  Detail & Related papers  (2020-07-05T08:16:03Z)

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.