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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Bibliographic Details
Published in:Applied sciences Vol. 15; no. 21; p. 11500
Main Authors: Eltanashi, Saeeda Meftah Salem, Kurnaz Türkben, Ayça
Format: Journal Article
Language:English
Published: Basel MDPI AG 01.11.2025
Subjects:
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
Online Access:Get full text
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Summary: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.
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ISSN:2076-3417
2076-3417
DOI:10.3390/app152111500