A Novel Machine Learning Approach for Central Nervous System Tumor Classification from DNA Methylation
Researchers propose a method combining Sparse Random Projection and multinomial logistic regression for CNS tumor classification from DNA methylation data. It achieves 96% accuracy on a reference cohort and 86% (91-class) and 93% (family-level) on an independent clinical cohort, outperforming state-of-the-art by 4-5 percentage points.
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[Submitted on 1 Jul 2026]
Title:A Novel Machine Learning Approach for Central Nervous System Tumor Classification from DNA Methylation
View a PDF of the paper titled A Novel Machine Learning Approach for Central Nervous System Tumor Classification from DNA Methylation, by Paulo R. Ferreira Jr. and 6 other authors
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Abstract:NA methylation profiling has become a powerful approach for central nervous system (CNS) tumor classification, yet important challenges remain regarding cross-cohort transferability, methodological correctness, and robust multiclass evaluation. In this work, we propose a novel and methodologically rigorous machine-learning approach for methylation-based CNS tumor classification that combines Sparse Random Projection for dimensionality reduction with multinomial logistic regression for classification. We evaluate the proposed approach in the same general experimental setting established by a widely used reference classifier. On the 2,801-sample reference cohort, our method achieves a mean accuracy of 96\% under stratified 3-fold cross-validation. On the independent 1,104-sample clinical evaluation cohort, it reaches 86\% accuracy at the 91-class level and 93\% when predictions are evaluated at the methylation class family level. These results improve upon the corresponding state-of-the-art reference figures of 82\% class-level concordance and 88\% family-level concordance, yielding absolute gains of approximately 4 and 5 percentage points, respectively. This improvement is clinically relevant: in a diagnostic setting, a 5-point increase in correct tumor classification can directly affect cancer subtype assignment and, in turn, influence treatment selection and downstream clinical decision-making. Our results show that the proposed model, grounded in stronger methodological practice in machine learning, consistently outperforms the previous state of the art across evaluation settings and can materially improve the reliability of CNS tumor classification.
Subjects:
Machine Learning (cs.LG); Genomics (q-bio.GN)
Cite as: arXiv:2607.01307 [cs.LG]
(or arXiv:2607.01307v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2607.01307
arXiv-issued DOI via DataCite
Submission history
From: Paulo Roberto Ferreira Jr. [view email] [v1] Wed, 1 Jul 2026 16:57:58 UTC (659 KB)
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