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 g...

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Vydáno v:	Applied sciences Ročník 15; číslo 21; s. 11500
Hlavní autoři:	Eltanashi, Saeeda Meftah Salem, Kurnaz Türkben, Ayça
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Basel MDPI AG 01.11.2025
Témata:	Antigens Artificial intelligence Biomedical research Classification Datasets early leukemia detection Forecasts and trends genomic data analysis Genomics Graft versus host disease graph neural networks (GNNs) Graph representations Histocompatibility antigens HLA histocompatibility antigens HLA typing Immunology Leukemia Leukocytes Machine learning Neural networks Patients Proteins Proteomics Single nucleotide polymorphisms SNP matching Stem cell transplantation Stem cells Support vector machines Transplantation
ISSN:	2076-3417, 2076-3417
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Shrnutí:	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.
Bibliografie:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14
ISSN:	2076-3417 2076-3417
DOI:	10.3390/app152111500