Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/112013
PIRA download icon_1.1View/Download Full Text
Title: Rethinking cancer gene identification through graph anomaly analysis
Authors: Zang, Y 
Ren, L
Li, Y
Wang, Z
Selby, DA
Wang, Z
Josef, S
Yin, H
Song, J
Wu, J
Issue Date: 2025
Source: The 39th Annual AAAI Conference on Artificial Intelligence, February 25 - March 4, 2025, Philadelphia, Pennsylvania, USA
Abstract: Graph neural networks (GNNs) have shown promise in integrating protein-protein interaction (PPI) networks for identifying cancer genes in recent studies. However, due to the insufficient modeling of the biological information in PPI networks, more faithfully depiction of complex protein interaction patterns for cancer genes within the graph structure remains largely unexplored. This study takes a pioneering step toward bridging biological anomalies in protein interactions caused by cancer genes to statistical graph anomaly. We find a unique graph anomaly exhibited by cancer genes, namely weight heterogeneity, which manifests as significantly higher variance in edge weights of cancer gene nodes within the graph. Additionally, from the spectral perspective, we demonstrate that the weight heterogeneity could lead to the "flattening out" of spectral energy, with a concentration towards the extremes of the spectrum. Building on these insights, we propose the HIerarchical-Perspective Graph Neural Network (HIPGNN) that not only determines spectral energy distribution variations on the spectral perspective, but also perceives detailed protein interaction context on the spatial perspective. Extensive experiments are conducted on two reprocessed datasets STRINGdb and CPDB, and the experimental results demonstrate the superiority of HIPGNN. Our code and data are released at https://github.com/zyl199710/HIPGNN.
Research Data: https://github.com/zyl199710/HIPGNN
Rights: Posted with permission of the author.
Appears in Collections:Conference Paper

Files in This Item:
File Description SizeFormat 
Zang_Rethinking_Cancer_Gene.pdf1.51 MBAdobe PDFView/Open
Open Access Information
Status open access
File Version Other Version
Show full item record

Page views

18
Citations as of Apr 1, 2025

Downloads

1
Citations as of Apr 1, 2025

Google ScholarTM

Check


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.