网络推理或重建算法在成功分析和识别组学命中之间的因果关系方面发挥着不可或缺的作用，以检测各种背景下失调和改变的信号成分，包括疾病状态和药物扰动。然而，信号网络的准确表示和复杂相互作用组中稀疏组学数据集中上下文特定相互作用的识别对综合方法提出了重大挑战。为了应对这些挑战，我们提出了 pyPARAGON（用于多组学数据集成的 Graphlet 引导网络上的 PAgeRANk-flux），这是一种将网络传播与 graphlet 相结合的新颖工具。 pyPARAGON 通过利用网络而不是依赖蛋白质之间的成对连接来提高准确性并最大限度地减少信号网络中非特异性相互作用的包含。通过对基准信号通路的综合评估，我们证明 pyPARAGON 在节点传播和边缘推理方面优于最先进的方法。此外，pyPARAGON 在发现癌症驱动网络方面表现出良好的性能。值得注意的是，我们通过将 105 个乳腺癌肿瘤的磷酸蛋白质组数据与相互作用组整合并展示肿瘤特异性信号通路，展示了其在基于网络的患者肿瘤分层中的实用性。总的来说，pyPARAGON 是一种在信号网络背景下分析和集成多组学数据的新颖工具。 pyPARAGON 可在 https://github.com/netlab-ku/pyPARAGON 获取。© 作者 2024。由牛津大学出版社出版。

Network inference or reconstruction algorithms play an integral role in successfully analyzing and identifying causal relationships between omics hits for detecting dysregulated and altered signaling components in various contexts, encompassing disease states and drug perturbations. However, accurate representation of signaling networks and identification of context-specific interactions within sparse omics datasets in complex interactomes pose significant challenges in integrative approaches. To address these challenges, we present pyPARAGON (PAgeRAnk-flux on Graphlet-guided network for multi-Omic data integratioN), a novel tool that combines network propagation with graphlets. pyPARAGON enhances accuracy and minimizes the inclusion of nonspecific interactions in signaling networks by utilizing network rather than relying on pairwise connections among proteins. Through comprehensive evaluations on benchmark signaling pathways, we demonstrate that pyPARAGON outperforms state-of-the-art approaches in node propagation and edge inference. Furthermore, pyPARAGON exhibits promising performance in discovering cancer driver networks. Notably, we demonstrate its utility in network-based stratification of patient tumors by integrating phosphoproteomic data from 105 breast cancer tumors with the interactome and demonstrating tumor-specific signaling pathways. Overall, pyPARAGON is a novel tool for analyzing and integrating multi-omic data in the context of signaling networks. pyPARAGON is available at https://github.com/netlab-ku/pyPARAGON.© The Author(s) 2024. Published by Oxford University Press.