In this modern era of precision medicine, molecular signatures identified from advanced omics technologies hold great promise to better guide clinical decisions. However, current approaches are often location-specific due to the inherent differences between platforms and across multiple centres, thus limiting the transferability of molecular signatures. We present Cross-Platform Omics Prediction (CPOP), a penalised regression model that can use omics data to predict patient outcomes in a platform-independent manner and across time and experiments. CPOP improves on the traditional prediction framework of using gene-based features by selecting ratio-based features with similar estimated effect sizes. These components gave CPOP the ability to have a stable performance across datasets of similar biology, minimising the effect of technical noise often generated by omics platforms. We present a comprehensive evaluation using melanoma transcriptomics data to demonstrate its potential to be used as a critical part of a clinical screening framework for precision medicine. Additional assessment of generalisation was demonstrated with ovarian cancer and inflammatory bowel disease studies.

在这个精准医学的现代时代，从先进的组学技术中鉴定出的分子特征对于更好地指导临床决策具有很大的希望。然而，由于平台之间和跨多个中心的固有差异，当前的方法通常是特定于位置的，因此限制了分子特征的可转移性。我们提出了跨平台组学预测 (CPOP)，这是一种惩罚回归模型，可以使用组学数据以独立于平台的方式以及跨时间和实验来预测患者的结果。CPOP 通过选择具有相似估计效应大小的基于比率的特征，改进了使用基于基因特征的传统预测框架。这些组件使 CPOP 能够在类似生物学的数据集中具有稳定的性能，最大限度地减少组学平台经常产生的技术噪音的影响。我们使用黑色素瘤转录组学数据进行了全面评估，以证明其有可能用作精准医学临床筛查框架的关键部分。卵巢癌和炎症性肠病研究证明了对泛化的额外评估。