用缺失数据训练的自编码器模型，提升皮肤肿瘤分类准确性
Masked Autoencoder Joint Learning for Robust Spitzoid Tumor Classification
- 设计可处理缺失甲基化数据的联合学习框架
- 在真实临床数据上分类准确率优于现有方法
- 适合做肿瘤精准诊断与不完整基因数据研究者
斯皮茨样肿瘤（ST）的准确诊断对预后至关重要，能避免治疗不足或过度治疗。表观遗传数据，尤其是DNA甲基化，为此提供了宝贵信息。然而，以往研究假设数据完整，而现实中甲基化谱常因覆盖度有限和实验误差存在缺失。本文挑战这一理想设定，提出ReMAC，作为ReMasker的扩展，用于高维数据在完整与不完整条件下的分类任务。在真实临床数据上的评估表明，相较于现有分类方法，ReMAC在斯皮茨样肿瘤分型中表现出更强且更稳健的性能。代码已开源：https://github.com/roshni-mahtani/ReMAC。
原文摘要 · Abstract (English)
Accurate diagnosis of spitzoid tumors (ST) is critical to ensure a favorable prognosis and to avoid both under- and over-treatment. Epigenetic data, particularly DNA methylation, provide a valuable source of information for this task. However, prior studies assume complete data, an unrealistic setting as methylation profiles frequently contain missing entries due to limited coverage and experimental artifacts. Our work challenges these favorable scenarios and introduces ReMAC, an extension of ReMasker designed to tackle classification tasks on high-dimensional data under complete and incomplete regimes. Evaluation on real clinical data demonstrates that ReMAC achieves strong and robust performance compared to competing classification methods in the stratification of ST. Code is available: https://github.com/roshni-mahtani/ReMAC.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。