Application of Graph Neural Networks to Model Stem Cell Donor–Recipient Compatibility in the Detection and Classification of Leukemia

Stem cell transplants are a common treatment for leukemia, and close donor–recipient matching improves their success. Machine learning models like support vector machine (SVM), convolutional neural networks (CNNs), and recurrent neural networks (RNNs) can have difficulty handling the complexity of genomic and immune data, which then lowers the accuracy of clinical predictions. This study looks at using graph neural networks (GNNs) in a different way. This method combines data such as single-nucleotide polymorphisms (SNPs), human leukocyte antigen (HLA) typing, and clinical details to create a graph that shows the relationship between donor and recipient pairs. The framework uses graph attention networks (GATs) to focus on key compatibility traits and Dynamic GNNs (DGNNs) to monitor changes in the immune system and the disease’s progression. With data from the 1000 Genomes Project, the model correctly identified matches with 97.68% to 99.74% accuracy and classified them with 98.76% to 99.4% accuracy, outperforming standard machine learning models. The model uses SNP similarity and HLA mismatches to assess compatibility, which enhances its match prediction and compatibility explanation capabilities. The results suggest that GNNs offer a helpful and understandable way to model donor–recipient matching, potentially assisting in early leukemia detection and personalized stem cell transplant plans.