合成卵巢癌多组学数据集：提出生物学导向的评估框架
Synthesis of Ovarian Cancer Multi-Omics Datasets: A Biology-Informed Evaluation Framework
一篇挺实在的方法论论文：不堆新模型，而是给合成多组学数据定了套可复用的评估流程，做医疗数据生成的可以拿来当参照。
arXiv 论文针对配对多组学癌症队列难以获取与共享的问题，研究合成数据的评估标准。作者提出一个生物学导向、患者不重叠的评估框架，用于检验配对的 RNA 表达与拷贝数变异（CNV）数据生成质量，并在 DECIDER 高级别浆液性卵巢癌纵向队列上做了案例研究。框架从模态保真度、RNA 与 CNV 配对一致性、训练数据邻近度三方面衡量，其中采用变分自编码器改造的三个多组学整合架构中，保真度与一致性得分最高的模型同样在训练数据邻近度上表现最好。
Synthesis of Ovarian Cancer Multi-Omics Datasets: A Biology-Informed Evaluation Framework
Paired multi-omic cancer cohorts are costly to assemble and difficult to share, motivating synthetic cohorts as a complementary resource. As generative model development accelerates, we shift the focus from proposing yet another architecture to defining evaluation criteria that determine whether synthetic data preserve biologically relevant structure. We introduce a biology-informed and patient-disjoint framework for evaluating the generation of paired RNA expression and copy-number variation (CNV) profiles, and apply it to the longitudinal DECIDER high-grade serous ovarian cancer cohort. The framework assesses modality-specific fidelity and coherence between RNA and CNV pairs using agreement between real training and held-out data as a reference, alongside complementary training-data proximity diagnostics. We adapt three multi-omic integration architectures based on variational autoencoders to generate paired profiles. The model leading several fidelity and coherence measures also shows the strongest training-data proximity. The protocol and case study offer the community a reference for comparing future multi-omic generators.