Refining Protein Co-Membership in Reactome: A Weighted Scoring Approach Based on Hierarchy and Sequences of Patwhays
In network biology, analyzing pathways is critical to study mechanisms or sequences of biological events. The pathway co-membership approach aims to link proteins belonging to the same pathway inside a network that is a clique. Although this method is great for projecting pathway knowledge onto proteins, it remains unsuitable for some downstream applications relying on graph distances like random…
In network biology, understanding pathways is crucial for studying biological events. The pathway co-membership approach links proteins within the same pathway, forming a clique. While this method effectively projects pathway knowledge onto proteins, it struggles with graph distances used in random walks with restart. To overcome this, researchers introduce a weighted pathway co-membership method that incorporates protein biological distances.
This is achieved through a Reactome pathway abstraction framework, capturing the hierarchical organization and sequential relationships of pathways. By assigning weights to these abstractions, a scoring system is defined for proteins within the clique. The approach is demonstrated using the Reactome pathway "Signaling by TGF-beta receptor complex" (R-HSA-170834), showcasing the generation of weighted co-membership cliques for the 29 Reactome top-level pathways.
The entire weighted pathway co-membership network is then constructed within the Reactome knowledge base. To validate and evaluate the method, it is tested against cancer hallmark gene sets and a random walk with restart experiment, comparing its performance to existing state-of-the-art methods. The results indicate that the weighted pathway co-membership approach surpasses current methods in gene prioritization for cancer hallmarks.
Written by urgent.news from bioRxiv's reporting — not their text. Machine-written — may contain errors; check the original before relying on it.
