卵巢癌多组学数据集的合成：一个生物学信息驱动的评估框架
Synthesis of Ovarian Cancer Multi-Omics Datasets: A Biology-Informed Evaluation Framework
Déborah Boyenval, Alina Malyutina, Jaana Oikkonen, Sammeli Liikkanen, Sampsa Hautaniemi
AI 速读 基于摘要
作者提出一个生物学信息驱动、患者不重叠的评估框架，用于评判合成配对RNA表达与CNV图谱是否保留生物学相关结构，并在DECIDER高级别浆液性卵巢癌纵向队列上开展案例研究。
cs.LG
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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.