这篇论文提出了一个能同时处理基因、蛋白质和miRNA数据的可解释深度学习框架，在乳腺癌生存预测上效果不错，适合关注多组学分析和可解释AI的读者。
该论文报道了Pathway Activity Autoencoders（PAA）框架，通过通路约束的架构实现多组学数据集成与可解释性。在乳腺癌数据集上，该方法在生存预测和亚型分类任务中验证了集成多组学数据的正面效果。分析表明，基因、蛋白质和miRNA表达层对最终性能贡献最大。重复性实验显示，dropout可提升模型鲁棒性，但过度正则化会降低预测性能。可视化展示了特征空间的临床相关性。
Biologically Informed Deep Neural Networks for Multi-Omic Integration, Pathway Activity Inference and Risk Stratification in Cancer
Integrating complex, multi-omics data presents significant challenges. Existing approaches often face a trade-off between model interpretability and representational capacity, with most either relying on post-hoc interpretation or use linear models that may overlook complex interactions. We report Pathway Activity Autoencoders for the multi-omics setting, which embed prior knowledge via pathway-informed architectural constraints, fostering interpretability, while preserving representational power. Our multi-omic framework is applied in the context of breast cancer and is evaluated in survival prediction and subtype classification with results indicating a positive effect of integration. We conduct analysis of individual omics layer impact on end-task performance, revealing that gene, protein, and microRNA expression layers provide the strongest contribution. Repeatability studies indicate that, while dropout improves model robustness and consistency, excessive regularisation can reduce predictive performance. Finally, visualizations of the learned feature space illustrate the framework's intrinsic transparency and clinical relevance. The results underscore the value of multi-omic integration and delineate the impact of individual omics layers, establishing practical guidelines for integration within our framework. Overall, our pathway activity autoencoder frameworks yield superior latent representations that are biologically meaningful and are directly translatable into clinically relevant insights.