Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
Permanent URI for this collectionhttps://hdl.handle.net/11147/7148
Browse
2 results
Search Results
Review Citation - Scopus: 2Wnt/β-catenin Signaling in Central Nervous System Regeneration(2025) Nazli, D.; Bora, U.; Ozhan, G.The Wnt/β-catenin signaling pathway plays a pivotal role in the development, maintenance, and repair of the central nervous system (CNS). This chapter explores the diverse functions of Wnt/β-catenin signaling, from its critical involvement in embryonic CNS development to its reparative and plasticity-inducing roles in response to CNS injury. We discuss how Wnt/β-catenin signaling influences various CNS cell types-astrocytes, microglia, neurons, and oligodendrocytes-each contributing to repair and plasticity after injury. The chapter also addresses the pathway's involvement in CNS disorders such as Alzheimer's and Parkinson's diseases, psychiatric disorders, and traumatic brain injury (TBI), highlighting potential Wnt-based therapeutic approaches. Lastly, zebrafish are presented as a promising model organism for studying CNS regeneration and neurodegenerative diseases, offering insights into future research and therapeutic development. © 2024. The Author(s), under exclusive license to Springer Nature Switzerland AG.Conference Object Explaining Graph Neural Network Predictions for Drug Repurposing(CEUR-WS, 2024) Loesch, J.; Yang, Y.; Ekmekci, P.; Dumontier, M.; Celebi, R.Graph Neural Networks (GNNs) are powerful tools for graph-related tasks, excelling in progressing graph-structured data while maintaining permutation invariance. However, their challenge lies in the obscurity of new node representations, hindering interpretability. This paper introduces a framework addressing this limitation by explaining GNN predictions. The proposed method takes any GNN prediction, for which it returns a concise subgraph as explanation. Utilizing Saliency Maps, an attribution gradient-based technique, we enhance interpretability by assigning importance scores to entities withing the knowledge graph via backpropagation. Evaluated on the Drug Repurposing Knowledge Graph, Graph Attention Network achieved a Hits@5 score of 0.451 and a Hits@10 score of 0.672. GraphSAGE demonstrated notable results with the highest recall rate of 0.992. Our framework underscores GNN efficacy and interpretability, which is crucial in complex scenarios like drug repurposing. Illustrated through an Alzheimer’s disease case study, our approach provides meaningful and comprehensible explanations for GNN predictions. This work contributes to advancing the transparency and utility of GNNs in real-world applications. © 2024 Copyright for this paper by its authors.