Attention-Enhanced Kolmogorov–Arnold Network for Interpretable Subtype Classification and Biomarker Discovery in Lower-Grade Glioma
DOI:
https://doi.org/10.52981/%20%20%20%20%20%20%20%20%20%20oiujas.v22i1.3490الكلمات المفتاحية:
Lower-grade glioma; Multi-omics integration; Kolmogorov–Arnold Network; Attention mechanism; Molecular subtype classification; Biomarker discovery; Deep learningالملخص
Precision molecular classification and valid biomedical marker identification are necessary for the optimization of personalized therapy in lower-grade glioma (LGG). In this study, we present a transparent deep learning model built on the Kolmogorov–Arnold Network (KAN) for effective LGG subtype classification and biomarker discovery. The method integrates multiple-omics (mRNA expression, DNA methylation and miRNA profiles) data from the TCGA-LGG sample set for comprehensive modelling of tumour heterogeneity. Before training the model, we performed variance-based feature screening, retaining molecular features and excluding class labels
التنزيلات
منشور
2026-01-18
كيفية الاقتباس
Mohammed Hamid, . . . . . . . . . . . . . E. ., Elbashir, . . . . . . . . . M. K., & Musa, . . . . . . . . . . M. E. . (2026). Attention-Enhanced Kolmogorov–Arnold Network for Interpretable Subtype Classification and Biomarker Discovery in Lower-Grade Glioma. Journal of Omdurman Islamic University Applied Sciences, 22(1), 144–170. https://doi.org/10.52981/ oiujas.v22i1.3490
