PathMoG: A Pathway-Centric Modular Graph Neural Network for Multi-Omics Survival Prediction
Learn how PathMoG, a modular graph neural network, predicts cancer survival from multi-omics data by integrating pathway-centric information, and apply this knowledge to improve your own survival prediction models
- Build a modular graph neural network using PathMoG as a reference
- Integrate KEGG-informed pathway modules into your model to capture gene interactions
- Apply Hierarchical Omics Modulation to condition gene-expression data
- Train and evaluate your model on multi-omics datasets for survival prediction
- Compare the performance of your model with existing survival prediction methods
Data scientists and researchers working on cancer genomics and survival prediction can benefit from this article, as it provides a novel approach to integrating multi-omics data for improved prediction accuracy
💡 Pathway-centric modular graph neural networks can effectively integrate multi-omics data for improved cancer survival prediction
🚀 Introducing PathMoG, a pathway-centric modular graph neural network for multi-omics survival prediction! 📈 Improve your cancer survival prediction models with this novel approach #PathMoG #CancerGenomics
Key Takeaways
Learn how PathMoG, a modular graph neural network, predicts cancer survival from multi-omics data by integrating pathway-centric information, and apply this knowledge to improve your own survival prediction models
Full Article
Abstract:
arXiv:2604.24371v1 Announce Type: cross Abstract: Cancer survival prediction from multi-omics data remains challenging because prognostic signals are high-dimensional, heterogeneous, and distributed across interacting genes and pathways. We propose PathMoG, a pathway-centric modular graph neural network for multi-omics survival prediction. PathMoG reorganizes genome-scale inputs into 354 KEGG-informed pathway modules, introduces a Hierarchical Omics Modulation module to condition gene-expression
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