Document Type : Original Research Paper

Authors

• Mahboubeh Ayoubi ¹
• Babak Teimourpour ²
• Mostafa Akhavan-Safar ³

¹ Independent Researcher, Suez, Egypt.

² IT department, Industrial & Systems Engineering, Tarbiat Modares university, Tehran, Iran.

³ Department of Computer and Information Technology Engineering, Payame Noor University (PNU), Tehran, Iran.

Abstract

Background and Objectives: Identifying and classifying cancer-driving genes by analyzing their complex relationships within gene regulatory networks (GRN) can significantly aid in the development, progression, and discovery of targeted cancer therapies. The cancer-driving genes are responsible for tumorigenesis and disease progression. However, the current methods frequently concentrate on network rebuilding, which restricts their capacity to identify regulatory linkages. By utilizing the structural and functional characteristics of GRNs, this work seeks to create a strong graph-based framework for precise cancer driver gene classification.
Methods: Network and graph-based methodologies are employed to analyze these complex gene networks. Using graph neural networks (GNN), complex intergenic patterns can be identified in genetic and cellular data. In this study, a GNN-based framework is proposed to classify genes in gene regulatory networks in order to improve the detection of cancer-driving genes. The proposed graph-based framework effectively integrates multi-omics data and mitigates class imbalance through an artificial oversampling strategy. The proposed GNN-based framework facilitates the modeling of both topological structure and feature information within gene interaction networks. Additionally, it addresses the challenges of class imbalance between driver and non-driver genes through the implementation of the GraphSMOTE technique.
Results: To construct the gene regulatory graph, the Regetworks regulatory dataset was combined with three gene expression datasets related to breast, lung, and colon cancers. The results demonstrate that the proposed model consistently attains robust classification performance, with AUC-ROC scores exceeding 0.77 in all cases and F1 scores above 0.70, outperforming previous network-based methods.
Conclusion: The evaluation criteria show that the proposed model has a high ability to generalize tumor types with differences in network topology and class imbalance.

Keywords

• Graph Neural Network
• Gene Regulatory Networks
• Node Classification
• Gen Cancer Detection

Main Subjects

• Bioinformatics

Open Access

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Publisher

Shahid Rajaee Teacher Training University