标题： 一种用于泛癌RNA-Seq分类的新型cVAE增强深度学习框架 显示英文标题

标题： A Novel cVAE-Augmented Deep Learning Framework for Pan-Cancer RNA-Seq Classification

摘要： 使用转录组（RNA-Seq）数据进行泛癌分类可以为肿瘤亚型和治疗选择提供信息，但由于维度极高和样本量有限而具有挑战性。 在本研究中，我们提出了一种新的深度学习框架，该框架使用类条件变分自编码器（cVAE）来增强泛癌基因表达分类的训练数据。 使用来自癌症基因组图谱（TCGA）的801个跨越5种癌症类型的肿瘤RNA-Seq样本，我们首先进行特征选择，将20,531个基因表达特征减少到500个变异程度最高的基因。 然后在这些数据上训练一个cVAE，以学习基于癌症类型的基因表达潜在表示，从而为每种肿瘤类别生成合成基因表达样本。 我们用这些cVAE生成的样本对训练集进行增强（使数据集大小翻倍），以减轻过拟合和类别不平衡问题。 随后在增强的数据集上训练一个两层多层感知机（MLP）分类器来预测肿瘤类型。 增强的框架在保留的测试集上实现了高分类准确率（约98%），明显优于仅在原始数据上训练的分类器。 我们展示了详细的实验结果，包括VAE训练曲线、分类器性能指标（ROC曲线和混淆矩阵）以及架构图，以说明该方法。 结果表明，基于cVAE的合成增强可以显著提高泛癌预测性能，尤其是对于代表性不足的癌症类别。 显示英文摘要

摘要： Pan-cancer classification using transcriptomic (RNA-Seq) data can inform tumor subtyping and therapy selection, but is challenging due to extremely high dimensionality and limited sample sizes. In this study, we propose a novel deep learning framework that uses a class-conditional variational autoencoder (cVAE) to augment training data for pan-cancer gene expression classification. Using 801 tumor RNA-Seq samples spanning 5 cancer types from The Cancer Genome Atlas (TCGA), we first perform feature selection to reduce 20,531 gene expression features to the 500 most variably expressed genes. A cVAE is then trained on this data to learn a latent representation of gene expression conditioned on cancer type, enabling the generation of synthetic gene expression samples for each tumor class. We augment the training set with these cVAE-generated samples (doubling the dataset size) to mitigate overfitting and class imbalance. A two-layer multilayer perceptron (MLP) classifier is subsequently trained on the augmented dataset to predict tumor type. The augmented framework achieves high classification accuracy (~98%) on a held-out test set, substantially outperforming a classifier trained on the original data alone. We present detailed experimental results, including VAE training curves, classifier performance metrics (ROC curves and confusion matrix), and architecture diagrams to illustrate the approach. The results demonstrate that cVAE-based synthetic augmentation can significantly improve pan-cancer prediction performance, especially for underrepresented cancer classes.