Revisiting the use of graph centrality models in biological pathway analysis

摘要

Biological pathways represent sets of bio-molecular entities, such as genes and proteins, and their cascades of interactions which associate with certain cellular functions [1]. The abundance and availability of annotated pathways is a key element in bridging the gap between molecular level dynamics and high-level biological insight [2–5]. Although changes in individual molecules may trigger variations in the cellular programs, many biological functions emerge from the systematic behaviour of entities and interactions. This systems biology concept positions the use of pathways in a significant value for discovery, treatment, diagnosis, and prediction in biomedical studies [6–9].