GMHAN: A Heterogeneous Graph Attention Framework for Prioritizing Coding and Non-coding Driver Genes

Clicks: 2
ID: 318019
2026
Free PDF
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This article has not been analysed, so there is no overall score — reader engagement is measured and shown alongside.
AI Quality Assessment
Not analyzed
Readership in this journal
Emerging

Ranked #665 of 829 articles by views in BMC Bioinformatics

Most read Least read

Bar heights use a square-root scale. Only the 120 most-read articles are drawn; the journal has 829 in total.

Mint this article as an NFT
Not yet minted

Create a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.

5 SUSD one-off · no wallet required
Sign in to mint
Abstract
MOTIVATION: Cancer, a disease of high complexity. Identifying cancer driver genes is fundamental for elucidating oncogenesis and promoting precision medicine. Currently, most approaches mainly focus on homogeneous gene networks and single-omics data, thereby mainly identifying coding driver genes while ignoring non-coding driver genes. RESULT: Thus, we introduced GMHAN, a novel framework based on HAN. Firstly, we integrated the three types of omics data of genes and PPI network topology feature, together with the multi-dimensional features of miRNAs. Afterwards, we used heterogeneous graph attention networks to obtain deep feature embeddings of genes and miRNAs. Finally, the deep feature embeddings are input multilayer perceptron to obtain the probability that genes and miRNAs being cancer drivers. In a comparative evaluation against seven methods, GMHAN demonstrates better performance across both pan-cancer and cancer-specific datasets, achieving higher scores in AUC and AUPR. It has confirmed its effectiveness in identifying carcinogenic drivers. AVAILABILITY AND IMPLEMENTATION: The source code of GMHAN is available at: https://github.com/mping315/GMHAN and https://doi.org/10.5281/zenodo.20154736.
Reference Key
openalex_W7165388973 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Ping Meng, Tianjiao Zhang, G Wang
Journal BMC Bioinformatics
Year 2026
DOI
10.1093/bioinformatics/btag403
URL
https://doi.org/10.1093/bioinformatics/btag403
Keywords Keywords not found

Citations

No citations found. To add a citation, contact the admin at info@scimatic.org

Comments

No comments yet. Be the first to comment on this article.