Evidence map›Paper›PMID 40413377›Full record

ArticleBMC bioinformatics2025

Gene2role: a role-based gene embedding method for comparative analysis of signed gene regulatory networks.

Xin Zeng, Shu Liu, Bowen Liu, Weihang Zhang, Wanzhe Xu, Fujio Toriumi, Kenta Nakai

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PubMed Full text at PMC Publisher
In one paragraph

Article in BMC bioinformatics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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0cells of the map it votes in
1citing papers in PubMed
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1 · What the graph read from it

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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3 · Its place in the literature

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1 citing paper in PubMed.

  1. Review
4 · The record

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5 · Who and what money

Authors and funding

7 authors.

Xin Zeng *Department of Computational Biology and Medical Sciences, The University of Tokyo, Kashiwa, 277-8563, Japan.
Shu Liu *Department of Systems Innovation, The University of Tokyo, Tokyo, 113-8654, Japan. liu-shu627@g.ecc.u-tokyo.ac.jp.
Bowen LiuDepartment of Computational Biology and Medical Sciences, The University of Tokyo, Kashiwa, 277-8563, Japan.
Weihang ZhangDepartment of Computational Biology and Medical Sciences, The University of Tokyo, Kashiwa, 277-8563, Japan.
Wanzhe XuDepartment of Computational Biology and Medical Sciences, The University of Tokyo, Kashiwa, 277-8563, Japan.
Fujio ToriumiDepartment of Systems Innovation, The University of Tokyo, Tokyo, 113-8654, Japan.
Kenta NakaiDepartment of Computational Biology and Medical Sciences, The University of Tokyo, Kashiwa, 277-8563, Japan. knakai@ims.u-tokyo.ac.jp.

Funding

Japan Science and Technology Gene topological representation in signed gene regulatory netwoAgency JPMJSP2108
6 · The paper itself

Abstract

backgroundUnderstanding the dynamics of gene regulatory networks (GRNs) across various cellular states is crucial for deciphering the underlying mechanisms governing cell behavior and functionality. However, current comparative analytical methods, which often focus on simple topological information such as the degree of genes, are limited in their ability to fully capture the similarities and differences among the complex GRNs.

resultsWe present Gene2role, a gene embedding approach that leverages multi-hop topological information from genes within signed GRNs. Initially, we demonstrated the effectiveness of Gene2role in capturing the intricate topological nuances of genes using GRNs inferred from four distinct data sources. Then, applying Gene2role to integrated GRNs allowed us to identify genes with significant topological changes across cell types or states, offering a fresh perspective beyond traditional differential gene expression analyses. Additionally, we quantified the stability of gene modules between two cellular states by measuring the changes in the gene embeddings within these modules.

conclusionsOur method augments the existing toolkit for probing the dynamic regulatory landscape, thereby opening new avenues for understanding gene behavior and interaction patterns across cellular transitions.

Indexed as

Computational BiologyGene Regulatory NetworksSoftwareAlgorithmsGene Expression ProfilingHumansGene module analysisGene regulatory network analysisGraph representation learning

Identifiers

PMID40413377
PMCPMC12103023

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