arXiv cs.AI· Md Zahangir Alom, Quynh T. Tran, Breuer Alexandar, Brent A. Orr·· 13 小时前AI 评分29
基于深度表征学习的 DNA 甲基化数据 OOD 检测提升临床分类器鲁棒性
Enhancing Clinical Classifier Robustness with Deep Representation Learning Based OOD Detection on DNA Methylation Data
AI 导读
研究提出一种面向 DNA 甲基化阵列数据的端到端深度表征学习 OOD 检测框架，在肉瘤与脑肿瘤队列上验证，其中 1D-SSA 方法效果最佳。肉瘤为分布内、脑肿瘤为分布外时 AUROC 达 0.975，TNR@95TPR 为 0.930；反向设置下 AUROC 为 0.9569。独立临床测试集上平均 AUROC 为 0.976，FPR@95TPR 为 0.065，可作为甲基化肿瘤分类系统的质控层。
正文 · 原文
Abstract:DNA methylation (DNAM) array-based tumor classification is a powerful clinical diagnostic tool, but models trained on fixed reference cohorts can produce faulty predictions for tumor types not represented in the training distribution. We propose an end-to-end deep representation learning-based out-of-distribution (OOD) detection framework for DNAm array data. Deep neural network (DNN) classifiers were trained on public sarcoma (SARC) (GSE140686) and brain tumor (BT) (GSE90496) cohorts, and feature vectors were extracted from bottleneck layers. Five OOD methods, including 1dimension (1D)-Subspace Analysis (1D-SSA), K-Nearest Neighbors (KNN), Ridge Regression, Mahalanobis Distance (MD), and Sum of Squared Distances (SSD), were explored with 200 and 500-dimensional embeddings. Spectral discrepancy was used to discriminate in-distribution (IND) from OOD samples. Performance was measured via cross-validation, holdout samples, and an independent clinical dataset. 1D-SSA was the most effective approach. In the sarcoma-IND/brain tumor-OOD setting, the framework achieved an AUROC of 0.975 and True Negative Rate (TNR)@95TPR of 0.930, and an AUROC of 0.9569 and TNR@95TPR of 0.8660 for the brain tumor-IND/sarcoma-OOD setting. On the independent clinical test set, the method yielded a mean AUROC of 0.976, a DE of 0.032, an FPR@95TPR of 0.065, and a TNR@95TPR of 0.935. These results show deep representation-based OOD detection as a practical quality-control layer for clinical-grade methylation-based tumor classification systems.
| Comments: | 11 pages, 8 figures |
| Subjects: | Genomics (q-bio.GN); Artificial Intelligence (cs.AI); Machine Learning (cs.LG) |
| Cite as: | arXiv:2610.36344 [q-bio.GN] |
| (or arXiv:2610.36344v1 [q-bio.GN] for this version) | |
| https://doi.org/10.48550/arXiv.2610.36344 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Md Zahangir Alom [view email]
[v1]
Mon, 26 Oct 2026 22:11:07 UTC (2,346 KB)
来源：arXiv cs.AI · arxiv.org