Causal Inference, Biomarker Discovery, Graph Neural Network, Feature Selection
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arXiv
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| Autori principali: | , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866911270138019840 |
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| author | Lan, Chaowang Wu, Jingxin Yuan, Yulong Liu, Chuxun Kang, Huangyi Liu, Caihua |
| author_facet | Lan, Chaowang Wu, Jingxin Yuan, Yulong Liu, Chuxun Kang, Huangyi Liu, Caihua |
| contents | Biomarker discovery from high-throughput transcriptomic data is crucial for advancing precision medicine. However, existing methods often neglect gene-gene regulatory relationships and lack stability across datasets, leading to conflation of spurious correlations with genuine causal effects. To address these issues, we develop a causal graph neural network (Causal-GNN) method that integrates causal inference with multi-layer graph neural networks (GNNs). The key innovation is the incorporation of causal effect estimation for identifying stable biomarkers, coupled with a GNN-based propensity scoring mechanism that leverages cross-gene regulatory networks. Experimental results demonstrate that our method achieves consistently high predictive accuracy across four distinct datasets and four independent classifiers. Moreover, it enables the identification of more stable biomarkers compared to traditional methods. Our work provides a robust, efficient, and biologically interpretable tool for biomarker discovery, demonstrating strong potential for broad application across medical disciplines. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_13295 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Causal Inference, Biomarker Discovery, Graph Neural Network, Feature Selection Lan, Chaowang Wu, Jingxin Yuan, Yulong Liu, Chuxun Kang, Huangyi Liu, Caihua Quantitative Methods Machine Learning Biomarker discovery from high-throughput transcriptomic data is crucial for advancing precision medicine. However, existing methods often neglect gene-gene regulatory relationships and lack stability across datasets, leading to conflation of spurious correlations with genuine causal effects. To address these issues, we develop a causal graph neural network (Causal-GNN) method that integrates causal inference with multi-layer graph neural networks (GNNs). The key innovation is the incorporation of causal effect estimation for identifying stable biomarkers, coupled with a GNN-based propensity scoring mechanism that leverages cross-gene regulatory networks. Experimental results demonstrate that our method achieves consistently high predictive accuracy across four distinct datasets and four independent classifiers. Moreover, it enables the identification of more stable biomarkers compared to traditional methods. Our work provides a robust, efficient, and biologically interpretable tool for biomarker discovery, demonstrating strong potential for broad application across medical disciplines. |
| title | Causal Inference, Biomarker Discovery, Graph Neural Network, Feature Selection |
| topic | Quantitative Methods Machine Learning |
| url | https://arxiv.org/abs/2511.13295 |