Improved the performance of pathway topology-based methods using Perti Net

Disruption of the normal functioning of cell signaling pathways frequently results in diseases.Precisely identifying disrupted signaling pathways is essential for understanding diseases. Pathwayanalysis methods have been developed for the specific purpose of identifying significantly disruptedsignaling pathways in a given condition. Among these methods, some take into account the topologiesof the pathways in their analysis, known as pathway topology-based (PT-based) methods. These typesof methods are superior to other types of pathway analysis methods, as they take into account theinternal structure of the pathway .PT-based methods model signaling pathways using graphs, which have limitations in capturing all typesof relations within a pathway. Research has demonstrated that modeling signaling pathways with Petrinets can address the limitations of graph-based modeling. PAPET is a method that employs Petri netmodeling and reports better results compared to other PT-based methods. However, the algorithm usedin the analysis of this method is excessively time-consuming .In this research, we attempted to simplify the algorithm used in PAPET. The proposed analysis can beincorporated as an additional layer preceding any PT-based method. We incorporate our proposedanalysis as an additional layer to SPIA and PADOG methods which are well-known PT-based methods.The results indicate that the additional layer would enhance the performance of the aforementionedmethods. Applying the target pathway technique on the benchmark of datasets from various diseasesshows that the proposed strategy prioritizes the target pathways better than other methods includingPAPET.

کلیدواژه ها:

Pathway analysis ، Enrichment analysis ، Petri net ، Pathway topology based