Institute of Biomedical Informatics, National Yang-Ming University, Taipei, Taiwan.
BMC Genomics. 2012;13 Suppl 7(Suppl 7):S25. doi: 10.1186/1471-2164-13-S7-S25. Epub 2012 Dec 13.
In biological systems, pathways coordinate or interact with one another to achieve a complex biological process. Studying how they influence each other is essential for understanding the intricacies of a biological system. However, current methods rely on statistical tests to determine pathway relations, and may lose numerous biologically significant relations.
This study proposes a method that identifies the pathway relations by measuring the functional relations between pathways based on the Gene Ontology (GO) annotations. This approach identified 4,661 pathway relations among 166 pathways from Pathway Interaction Database (PID). Using 143 pathway interactions from PID as testing data, the function-based approach (FBA) is able to identify 93% of pathway interactions, better than the existing methods based on the shared components and protein-protein interactions. Many well-known pathway cross-talks are only identified by FBA. In addition, the false positive rate of FBA is significantly lower than others via pathway co-expression analysis.
This function-based approach appears to be more sensitive and able to infer more biologically significant and explainable pathway relations.
在生物系统中，途径相互协调或相互作用以实现复杂的生物学过程。研究它们如何相互影响对于理解生物系统的复杂性至关重要。然而，目前的方法依赖于统计检验来确定途径关系，可能会丢失许多具有生物学意义的关系。
本研究提出了一种方法，通过基于基因本体论（GO）注释测量途径之间的功能关系来识别途径关系。该方法在来自途径相互作用数据库（PID）的 166 条途径中鉴定了 4661 条途径关系。使用 PID 中的 143 条途径相互作用作为测试数据，基于功能的方法（FBA）能够识别 93%的途径相互作用，优于基于共享组件和蛋白质-蛋白质相互作用的现有方法。许多众所周知的途径串扰仅通过 FBA 识别。此外，FBA 的假阳性率通过途径共表达分析显著低于其他方法。
这种基于功能的方法似乎更敏感，能够推断出更具生物学意义和可解释的途径关系。
