{
  "id": 12485866,
  "title": "Refining Protein Co-Membership in Reactome: A Weighted Scoring Approach Based on Hierarchy and Sequences of Patwhays",
  "url": "https://urgent.news/2026/10/06/refining-protein-co-membership-in-reactome-a-weighted-scoring",
  "topic": "science",
  "section": "Science",
  "published": "2026-10-06T00:00:00.000Z",
  "source": {
    "name": "bioRxiv",
    "slug": "biorxiv",
    "url": "https://www.biorxiv.org/content/10.64898/2026.09.30.755656v1?rss=1"
  },
  "original_language": "en",
  "account": "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.",
  "summary": "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…",
  "key_points": [],
  "editors_take": null,
  "illustration": null,
  "coverage": {
    "outlets": 1,
    "also_reported_by": []
  },
  "ai_generated": true,
  "disclaimer": "Summaries, key points and the editor’s take are written by software from other outlets’ reporting and may contain errors — always check the linked original."
}