GNN-Suite: a Graph Neural Network Benchmarking Framework for Biomedical Informatics

• URL: http://arxiv.org/abs/2505.10711v1
• Date: Thu, 15 May 2025 21:14:30 GMT
• Title: GNN-Suite: a Graph Neural Network Benchmarking Framework for Biomedical Informatics
• Authors: Sebestyén Kamp, Giovanni Stracquadanio, T. Ian Simpson,
• Abstract summary: We present GNN-Suite, a framework for constructing and benchmarking Graph Neural Network (GNN) architectures in computational biology.<n>We demonstrate its utility in identifying cancer-driver genes by constructing molecular networks from protein-protein interaction (PPI) data.<n>Our results show that a common framework for implementing and evaluating GNN architectures aids in identifying not only the best model but also the most effective means of incorporating complementary data.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We present GNN-Suite, a robust modular framework for constructing and benchmarking Graph Neural Network (GNN) architectures in computational biology. GNN-Suite standardises experimentation and reproducibility using the Nextflow workflow to evaluate GNN performance. We demonstrate its utility in identifying cancer-driver genes by constructing molecular networks from protein-protein interaction (PPI) data from STRING and BioGRID and annotating nodes with features from the PCAWG, PID, and COSMIC-CGC repositories. Our design enables fair comparisons among diverse GNN architectures including GAT, GAT3H, GCN, GCN2, GIN, GTN, HGCN, PHGCN, and GraphSAGE and a baseline Logistic Regression (LR) model. All GNNs were configured as standardised two-layer models and trained with uniform hyperparameters (dropout = 0.2; Adam optimiser with learning rate = 0.01; and an adjusted binary cross-entropy loss to address class imbalance) over an 80/20 train-test split for 300 epochs. Each model was evaluated over 10 independent runs with different random seeds to yield statistically robust performance metrics, with balanced accuracy (BACC) as the primary measure. Notably, GCN2 achieved the highest BACC (0.807 +/- 0.035) on a STRING-based network, although all GNN types outperformed the LR baseline, highlighting the advantage of network-based learning over feature-only approaches. Our results show that a common framework for implementing and evaluating GNN architectures aids in identifying not only the best model but also the most effective means of incorporating complementary data. By making GNN-Suite publicly available, we aim to foster reproducible research and promote improved benchmarking standards in computational biology. Future work will explore additional omics datasets and further refine network architectures to enhance predictive accuracy and interpretability in biomedical applications.

Related papers

• Graph Neural Networks Powered by Encoder Embedding for Improved Node Learning [17.31465642587528]
  Graph neural networks (GNNs) have emerged as a powerful framework for a wide range of node-level graph learning tasks.<n>In this paper, we leverage a statistically grounded method, one-hot graph encoder embedding (GEE), to generate high-quality initial node features.<n>We demonstrate its effectiveness through extensive simulations and real-world experiments across both unsupervised and supervised settings.
  arXiv  Detail & Related papers  (2025-07-15T21:01:54Z)
• RelGNN: Composite Message Passing for Relational Deep Learning [56.48834369525997]
  We introduce RelGNN, a novel GNN framework specifically designed to capture the unique characteristics of relational databases.<n>At the core of our approach is the introduction of atomic routes, which are sequences of nodes forming high-order tripartite structures.<n>RelGNN consistently achieves state-of-the-art accuracy with up to 25% improvement.
  arXiv  Detail & Related papers  (2025-02-10T18:58:40Z)
• Efficient Heterogeneous Graph Learning via Random Projection [58.4138636866903]
  Heterogeneous Graph Neural Networks (HGNNs) are powerful tools for deep learning on heterogeneous graphs. Recent pre-computation-based HGNNs use one-time message passing to transform a heterogeneous graph into regular-shaped tensors. We propose a hybrid pre-computation-based HGNN, named Random Projection Heterogeneous Graph Neural Network (RpHGNN)
  arXiv  Detail & Related papers  (2023-10-23T01:25:44Z)
• Global Minima, Recoverability Thresholds, and Higher-Order Structure in GNNS [0.0]
  We analyze the performance of graph neural network (GNN) architectures from the perspective of random graph theory. We show how both specific higher-order structures in synthetic data and the mix of empirical structures in real data have dramatic effects on GNN performance.
  arXiv  Detail & Related papers  (2023-10-11T17:16:33Z)
• T-GAE: Transferable Graph Autoencoder for Network Alignment [79.89704126746204]
  T-GAE is a graph autoencoder framework that leverages transferability and stability of GNNs to achieve efficient network alignment without retraining. Our experiments demonstrate that T-GAE outperforms the state-of-the-art optimization method and the best GNN approach by up to 38.7% and 50.8%, respectively.
  arXiv  Detail & Related papers  (2023-10-05T02:58:29Z)
• GNN-Ensemble: Towards Random Decision Graph Neural Networks [3.7620848582312405]
  Graph Neural Networks (GNNs) have enjoyed wide spread applications in graph-structured data. GNNs are required to learn latent patterns from a limited amount of training data to perform inferences on a vast amount of test data. In this paper, we push one step forward on the ensemble learning of GNNs with improved accuracy, robustness, and adversarial attacks.
  arXiv  Detail & Related papers  (2023-03-20T18:24:01Z)
• Exploiting Neighbor Effect: Conv-Agnostic GNNs Framework for Graphs with Heterophily [58.76759997223951]
  We propose a new metric based on von Neumann entropy to re-examine the heterophily problem of GNNs. We also propose a Conv-Agnostic GNN framework (CAGNNs) to enhance the performance of most GNNs on heterophily datasets.
  arXiv  Detail & Related papers  (2022-03-19T14:26:43Z)
• AdaGCN:Adaptive Boosting Algorithm for Graph Convolutional Networks on Imbalanced Node Classification [10.72543417177307]
  We propose an ensemble model called AdaGCN, which uses a Graph Convolutional Network (GCN) as the base estimator during adaptive boosting. Our model also improves state-of-the-art baselines on all of the challenging node classification tasks we consider.
  arXiv  Detail & Related papers  (2021-05-25T02:43:31Z)
• A Unified Lottery Ticket Hypothesis for Graph Neural Networks [82.31087406264437]
  We present a unified GNN sparsification (UGS) framework that simultaneously prunes the graph adjacency matrix and the model weights. We further generalize the popular lottery ticket hypothesis to GNNs for the first time, by defining a graph lottery ticket (GLT) as a pair of core sub-dataset and sparse sub-network.
  arXiv  Detail & Related papers  (2021-02-12T21:52:43Z)
• Policy-GNN: Aggregation Optimization for Graph Neural Networks [60.50932472042379]
  Graph neural networks (GNNs) aim to model the local graph structures and capture the hierarchical patterns by aggregating the information from neighbors. It is a challenging task to develop an effective aggregation strategy for each node, given complex graphs and sparse features. We propose Policy-GNN, a meta-policy framework that models the sampling procedure and message passing of GNNs into a combined learning process.
  arXiv  Detail & Related papers  (2020-06-26T17:03:06Z)
• Adaptive Universal Generalized PageRank Graph Neural Network [36.850433364139924]
  Graph neural networks (GNNs) are designed to exploit both sources of evidence but they do not optimally trade-off their utility. We introduce a new Generalized PageRank (GPR) GNN architecture that adaptively learns the GPR weights. GPR-GNN offers significant performance improvement compared to existing techniques on both synthetic and benchmark data.
  arXiv  Detail & Related papers  (2020-06-14T19:27:39Z)
• Bayesian Graph Neural Networks with Adaptive Connection Sampling [62.51689735630133]
  We propose a unified framework for adaptive connection sampling in graph neural networks (GNNs) The proposed framework not only alleviates over-smoothing and over-fitting tendencies of deep GNNs, but also enables learning with uncertainty in graph analytic tasks with GNNs.
  arXiv  Detail & Related papers  (2020-06-07T07:06:35Z)
• Self-Enhanced GNN: Improving Graph Neural Networks Using Model Outputs [20.197085398581397]
  Graph neural networks (GNNs) have received much attention recently because of their excellent performance on graph-based tasks. We propose self-enhanced GNN (SEG), which improves the quality of the input data using the outputs of existing GNN models. SEG consistently improves the performance of well-known GNN models such as GCN, GAT and SGC across different datasets.
  arXiv  Detail & Related papers  (2020-02-18T12:27:16Z)

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.