The search for prognostic biomarkers capable of predicting patient outcomes, by analyzing gene expression in tissue samples and other molecular profiles, remains largely focused on single-gene-based or global-gene-search approaches. Gene-centric approaches, while foundational, fail to capture the higher-order dependencies that reflect the activities of co-regulated processes, pathway alterations, and regulatory networks, all of which are crucial in determining the patient outcomes in complex diseases like cancer. Here, we introduce GPS-Net, a computational framework that fills the gap in efficiently identifying prognostic modules by incorporating the holistic pathway structures and the network of gene interactions. By innovatively incorporating advanced multiple kernel learning techniques and network-based regularization, the proposed method not only enhances the accuracy of biomarker and pathway identification but also significantly reduces computational complexity, as demonstrated by extensive simulation studies. Applying GPS-Net, we identified key pathways that are predictive of patient outcomes in a cancer immunotherapy study. Overall, our approach provides a novel framework that renders genome-wide pathway-level prognostic analysis both feasible and scalable, synergizing both mechanism-driven and data-driven methodologies for precision genomics.

中文翻译：

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GPS-Net：基于网络正则化核学习发现预后通路模块


通过分析组织样本和其他分子谱中的基因表达来寻找能够预测患者预后的预后生物标志物，仍然主要集中在基于单基因或全局基因搜索的方法上。以基因为中心的方法虽然是基础，但未能捕捉到反映共同调节过程、通路改变和调节网络活动的高阶依赖关系，所有这些都对于确定癌症等复杂疾病的患者预后至关重要。在这里，我们介绍了 GPS-Net，这是一个计算框架，通过整合整体通路结构和基因相互作用网络，填补了有效识别预后模块的空白。通过创新性地结合先进的多核学习技术和基于网络的正则化，所提出的方法不仅提高了生物标志物和通路识别的准确性，而且显著降低了计算复杂性，正如广泛的仿真研究所证明的那样。应用 GPS-Net，我们确定了在癌症免疫治疗研究中预测患者预后的关键途径。总体而言，我们的方法提供了一个新颖的框架，使全基因组通路水平预后分析既可行又可扩展，将机制驱动和数据驱动的方法协同用于精准基因组学。