20.500.12556/DiRROS-22648 Graph Convolutional Networks for Predicting Cancer Outcomes and Stage a focus on cGAS-STING pathway activation The study presented in this paper evaluated gene expression profiles from The Cancer Genome Atlas (TCGA). To reduce complexity, we focused on genes in the cGAS–STING pathway, crucial for cytosolic DNA detection and immune response. The study analyzes three clinical variables: disease-specific survival (DSS), overall survival (OS), and tumor stage. To effectively utilize the high-dimensional gene expression data, we needed to find a way to project these data meaningfully. Since gene pathways can be represented as graphs, a novel method of presenting genomics data using graph data structure was employed, rather than the conventional tabular format. To leverage the gene expression data represented as graphs, we utilized a graph convolutional network (GCN) machine learning model in conjunction with the genetic algorithm optimization technique. This allowed for obtaining an optimal graph representation topology and capturing important activations within the pathway for each use case, enabling a more insightful analysis of the cGAS–STING pathway and its activations across different cancer types and clinical variables. To tackle the problem of unexplainable AI, graph visualization alongside the integrated gradients method was employed to explain the GCN model’s decision-making process, identifying key nodes (genes) in the cGAS–STING pathway. This approach revealed distinct molecular mechanisms, enhancing interpretability. This study demonstrates the potential of GCNs combined with explainable AI to analyze gene expression, providing insights into cancer progression. Further research with more data is needed to validate these findings. cGAS–STING graph-convolutional-network graphs cancer pan-cancer machine learning NGS cGAS–STING grafična konvolucijska mreža grafi rak pan-rak strojno učenje NGS true false true MDPI Angleški jezik Slovenski jezik © 2024 by the authors Neznano 2025-06-14 03:37:01 2025-09-09 10:21:12 2025-10-24 03:51:52 0000-00-00 00:00:00 2024 0 0 Nasl. z nasl. zaslona; Opis vira z dne 17. 9. 2024; Soavtorji: Borna Skračić, Danijel Kučak and Leo Mršić; str. 2033–2048 iss. 3 Vol. 6 2024 0000-00-00 Zaloznikova Objavljeno NiDoloceno 0000-00-00 0000-00-00 2024-09-11 004.85:616.9 2504-4990 10.3390/make6030100 207936515 https://www.mdpi.com/2504-4990/6/3/100 1 4f198df7-48af-11f0-bf0a-001a4af901a5 https://dirros.openscience.si/Dokument.php?lang=slv&id=32821 RAZ_Sokac_Mateo_2024.pdf RAZ_Sokac_Mateo_2024.pdf 1 B307E0E3A9A96ED45AE9D3801F61F6C8 c39ee6fee9364421ca3fd33776a093095133e37355733702496ccc6a7f5db15e aaf2f7ca-8d55-11f0-9bb7-001a4af901a5 https://dirros.openscience.si/Dokument.php?lang=slv&id=34848 Rudolfovo – Znanstveno in tehnološko središče Novo mesto 0 0 0