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| Main Authors: | , , |
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| Format: | Preprint |
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2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2504.10343 |
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| _version_ | 1866912326637060096 |
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| author | Padron-Manrique, Cristian Valdez, Juan José Oropeza Resendis-Antonio, Osbaldo |
| author_facet | Padron-Manrique, Cristian Valdez, Juan José Oropeza Resendis-Antonio, Osbaldo |
| contents | Tissue-of-origin signals dominate pan-cancer gene expression, often obscuring molecular features linked to patient survival. This hampers the discovery of generalizable biomarkers, as models tend to overfit tissue-specific patterns rather than capture survival-relevant signals. To address this, we propose a Domain-Adversarial Neural Network (DANN) trained on TCGA RNA-seq data to learn representations less biased by tissue and more focused on survival. Identifying tissue-independent genetic profiles is key to revealing core cancer programs. We assess the DANN using: (1) Standard SHAP, based on the original input space and DANN's mortality classifier; (2) A layer-aware strategy applied to hidden activations, including an unsupervised manifold from raw activations and a supervised manifold from mortality-specific SHAP values. Standard SHAP remains confounded by tissue signals due to biases inherent in its computation. The raw activation manifold was dominated by high-magnitude activations, which masked subtle tissue and mortality-related signals. In contrast, the layer-aware SHAP manifold offers improved low-dimensional representations of both tissue and mortality signals, independent of activation strength, enabling subpopulation stratification and pan-cancer identification of survival-associated genes. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_10343 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Domain-Adversarial Neural Network and Explainable AI for Reducing Tissue-of-Origin Signal in Pan-cancer Mortality Classification Padron-Manrique, Cristian Valdez, Juan José Oropeza Resendis-Antonio, Osbaldo Machine Learning Quantitative Methods Tissue-of-origin signals dominate pan-cancer gene expression, often obscuring molecular features linked to patient survival. This hampers the discovery of generalizable biomarkers, as models tend to overfit tissue-specific patterns rather than capture survival-relevant signals. To address this, we propose a Domain-Adversarial Neural Network (DANN) trained on TCGA RNA-seq data to learn representations less biased by tissue and more focused on survival. Identifying tissue-independent genetic profiles is key to revealing core cancer programs. We assess the DANN using: (1) Standard SHAP, based on the original input space and DANN's mortality classifier; (2) A layer-aware strategy applied to hidden activations, including an unsupervised manifold from raw activations and a supervised manifold from mortality-specific SHAP values. Standard SHAP remains confounded by tissue signals due to biases inherent in its computation. The raw activation manifold was dominated by high-magnitude activations, which masked subtle tissue and mortality-related signals. In contrast, the layer-aware SHAP manifold offers improved low-dimensional representations of both tissue and mortality signals, independent of activation strength, enabling subpopulation stratification and pan-cancer identification of survival-associated genes. |
| title | Domain-Adversarial Neural Network and Explainable AI for Reducing Tissue-of-Origin Signal in Pan-cancer Mortality Classification |
| topic | Machine Learning Quantitative Methods |
| url | https://arxiv.org/abs/2504.10343 |