A personalized pathway activation inference method based on pathway structure for classification of inflammatory bowel disease

Inflammatory bowel disease (IBD) is a complex disease that mainly consists of two subtypes, ulcerative colitis (UC) and Crohn's disease (CD). These two diseases exhibit similar clinical symptoms, leading to a potential misdiagnosis of patients. Accurately assessing the disease status and identifying specific biomarkers are important for the diagnosis and treatment of IBD. Pathways play a significant role in the occurrence and progression of complex diseases, involving the abnormal functionality or regulatory imbalance. Although many methods integrating pathway information have been proposed to evaluate pathway activity, but these methods rarely take into account the topology of pathway networks. Some algorithms based on pathway structure do not de-noise the pathway networks and characterize the disease-specific state of a single sample from the perspective of pathways. In this study, we present a personalized pathway activation inference method (PPA-PS) based on the pathway structure, which utilizes the topology of pathways to evaluate the importance of nodes and quantify the degree of edge disturbance caused by a single disease sample. The results demonstrate that PPA-PS outperforms the compared approaches in terms of classification performance and robustness, indicating its potential as a valuable tool for the pathway biomarker identification and precise diagnosis of IBD.