Graduate Institute of Biomedical Informatics, Taipei Medical University, 250 Wu-Hsing Street, Taipei 11031, Taiwan.
Graduate Institute of Biomedical Informatics, Taipei Medical University, 250 Wu-Hsing Street, Taipei 11031, Taiwan.
Comput Biol Med. 2014 Aug;51:111-21. doi: 10.1016/j.compbiomed.2014.04.023. Epub 2014 May 14.
Protein-protein interactions (PPIs) and gene expression profiles interact with each other in the regulation of a pathway. Many studies have expressed the feasibility of deriving the pathway from the PPI network or gene expression information. However, previous researches are still limited to a small region of large-scale genomics and whole-proteomics. Furthermore, the gene information induced by diseases had not been considered yet in such researches. In this study, we propose an approach to find potential fragments of active pathways related to various stages of diseases by a top-rank score-based method, integrating PPI network and gene expression change information. Validation of produced pathway maps is performed by mapping with KEGG renal cell carcinoma (RCC) map. The pathway maps of RCC are built and three key genes are found. The accuracies of coverage ratio of the produced pathway map are 50% and 48.48%. In this case, the hubs that link the nodes from RCC provide a valuable guide for further studies for understanding RCC. In conclusion, the pathway map co-constructed by this proposed method can provide more insight than limited subnetwork biomarkers.
蛋白质-蛋白质相互作用 (PPIs) 和基因表达谱在通路调控中相互作用。许多研究已经表达了从 PPI 网络或基因表达信息中推导出通路的可行性。然而，以前的研究仍然局限于大规模基因组学和全蛋白质组学的一小部分。此外，这些研究尚未考虑疾病引起的基因信息。在这项研究中，我们提出了一种通过基于排名的评分方法整合 PPI 网络和基因表达变化信息来寻找与疾病各个阶段相关的潜在活性通路片段的方法。通过与 KEGG 肾细胞癌 (RCC) 图谱进行映射来验证产生的通路图谱。构建了 RCC 的通路图谱，并发现了三个关键基因。生成的通路图谱的覆盖比的准确率为 50% 和 48.48%。在这种情况下，将 RCC 节点连接起来的枢纽为进一步研究 RCC 提供了有价值的指导。总之，该方法共同构建的通路图谱比有限的子网络生物标志物提供了更深入的见解。
