Graph neural network model GGDisnet for identifying genes in gastrointestinal cancer and single-cell analysis

• Published in: Computers in biology and medicine Vol. 172; p. 108285
• Main Authors: Wang, Ying, Du, Yaqi
• Format: Journal Article
• Language: English
• Published: United States Elsevier Ltd 01.04.2024; Elsevier Limited
• Subjects: Cancer; Classification; Colorectal cancer; Comparative analysis; Data analysis; Datasets; Gastric cancer; Gastrointestinal cancer; Gastrointestinal Neoplasms - genetics; Gene expression; Genes; Graph neural networks; Humans; Internal Medicine; Keywords; Machine learning; Medical research; Neural networks; Neural Networks, Computer; Other; Proteins; Single-Cell Analysis
• ISSN: 0010-4825; 1879-0534; 1879-0534
• DOI: 10.1016/j.compbiomed.2024.108285

Gastrointestinal cancer, a highly prevalent form of cancer, has been the subject of extensive research resulting in the identification of numerous pathogenic genes. However, validation and exploration of these findings often require traditional biological experiments, which are time-consuming and limit the ability to make extensive assessments promptly. To address this challenge, this paper introduces GGDisnet, a novel model for identifying genes associated with gastrointestinal cancer. GGDisnet efficiently screens human genes, providing a set of genes with a high correlation to gastrointestinal cancer for reference. Comparative analysis with other models demonstrates GGDisnet's superior performance. Furthermore, we conducted enrichment and single-cell analyses based on GGDisnet-predicted genes, offering valuable clinical insights. •Created GGDisnet, a swift predictive model for numerous genes linked to digestive tract cancers.•Conducted enrichment analysis on gastrointestinal cancer-related genes, identifying crucial pathways.•Visualized and analyzed predicted genes using single-cell sequencing data, offering clinical insights into the distribution of gastrointestinal cancer-related genes.