评估基于单组学和多组学的可解释人工智能（MOXAI）用于成人型弥漫性胶质瘤分子亚类分类
Evaluating Single and Multi-Omics Based Explainable Artificial Intelligence (MOXAI) for Molecular Subclass Classification of Adult-Type Diffuse Gliomas
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中文总结 AI 辅助
本文提出MOXAI框架，整合DNA甲基化和拷贝数数据，利用深度学习模型分类成人型弥漫性胶质瘤分子亚型，实现高准确率并具备可解释性。
中文摘要 AI 辅助
DNA甲基化（DNAM）谱分析已成为分类脑肿瘤和实体肿瘤的强大诊断工具。然而，现有的计算模型通常分别分析甲基化和拷贝数变异（CNV）数据，未能捕获其整合所能提供的互补信息。此外，当前的分类模型缺乏类似于传统肿瘤分级的类内风险评估机制，并且没有既定的可解释性方法能够将分类决策归因于特定的基因组位点。在本文中，我们提出了MOXAI（基于多组学的可解释人工智能），这是一个深度学习框架，整合来自甲基化阵列的DNA甲基化和拷贝数数据，用于分类成人型弥漫性胶质瘤的分子亚型，同时提供单模态变体以进行比较。使用来自癌症基因组图谱（TCGA）的队列，我们在仅甲基化数据、仅拷贝数数据以及组合多模态数据上训练了ResNet50、DINOv2和图注意力网络（GAT）模型。我们进一步开发了基于类激活映射（CAMs）和梯度加权CAM（Grad-CAM）的可解释人工智能（XAI）方法，以识别与每个分类决策最相关的特定CpG位点、基因和染色体区域。多模态模型实现了高达92.98%的交叉验证准确率，优于仅基于CNV数据训练的模型。DINOv2表现出最强的泛化能力，在独立验证集上达到94.25%的准确率（置信度>0.9）。XAI结果与成人型弥漫性胶质瘤亚型的既定分子特征一致，确认了该框架的生物学可解释性。
英文摘要
DNA methylation (DNAM) profiling has emerged as a powerful diagnostic tool for classifying brain and solid tumors. However, existing computational models typically analyze methylation and copy number variation (CNV) data separately, failing to capture the complementary information their integration could provide. Moreover, current classification models lack mechanisms for within-class risk assessment analogous to traditional tumor grading, and no established explainability method can attribute classification decisions to specific genomic loci. In this paper, we present MOXAI (Multi-Omics Based Explainable AI), a deep learning framework that integrates DNA methylation and copy number data from methylation arrays to classify molecular subtypes of adult-type diffuse gliomas, alongside single-modality variants for comparison. Using a cohort from The Cancer Genome Atlas (TCGA), we trained ResNet50, DINOv2, and Graph Attention Network (GAT) models on methylation data alone, copy number data alone, and combined multimodal data. We further developed explainable AI (XAI) methods based on class activation maps (CAMs) and gradient-weighted CAM (Grad-CAM) to identify the specific CpG sites, genes, and chromosomal regions most relevant to each classification decision. The multimodal model achieved up to 92.98% cross-validation accuracy, outperforming models trained on CNV data alone. DINOv2 showed the strongest generalization, reaching 94.25% accuracy (confidence >0.9) on independent validation sets. XAI results aligned with established molecular features of adult-type diffuse glioma subtypes, confirming the biological interpretability of the framework.
发表机构
- St. Jude Children’s Research Hospital（圣裘德儿童研究医院）
机构由 AI 辅助整理，请以论文原文为准。
