标题： OmicsCL：无监督对比学习在癌症亚型发现和生存分层中的应用 显示英文标题

标题： OmicsCL: Unsupervised Contrastive Learning for Cancer Subtype Discovery and Survival Stratification

摘要： 无监督学习从多组学数据中识别疾病亚型为推进个性化医学提供了显著机会。 我们介绍了OmicsCL，一个模块化的对比学习框架，该框架联合嵌入了不同类型的组学模态（如基因表达、DNA甲基化和miRNA表达）到一个统一的潜在空间。 我们的方法结合了一个生存感知对比损失，鼓励模型学习与生存相关模式对齐的表示，而无需依赖标记的结果。 在TCGA BRCA数据集上评估时，OmicsCL揭示了临床上有意义的聚类，并在患者生存方面实现了强大的无监督一致性。 该框架在超参数配置中表现出鲁棒性，并且可以调整以优先考虑亚型一致性或生存分层。 消融研究确认，整合生存感知损失显著增强了学习表示的预测能力。 这些结果强调了对比目标在高维异质组学数据中发现生物学洞见的前景。 显示英文摘要

摘要： Unsupervised learning of disease subtypes from multi-omics data presents a significant opportunity for advancing personalized medicine. We introduce OmicsCL, a modular contrastive learning framework that jointly embeds heterogeneous omics modalities-such as gene expression, DNA methylation, and miRNA expression-into a unified latent space. Our method incorporates a survival-aware contrastive loss that encourages the model to learn representations aligned with survival-related patterns, without relying on labeled outcomes. Evaluated on the TCGA BRCA dataset, OmicsCL uncovers clinically meaningful clusters and achieves strong unsupervised concordance with patient survival. The framework demonstrates robustness across hyperparameter configurations and can be tuned to prioritize either subtype coherence or survival stratification. Ablation studies confirm that integrating survival-aware loss significantly enhances the predictive power of learned embeddings. These results highlight the promise of contrastive objectives for biological insight discovery in high-dimensional, heterogeneous omics data.