Gene-MOE：一种利用泛癌基因组信息的稀疏门控预后与分类框架
Gene-MOE: A sparsely gated prognosis and classification framework exploiting pan-cancer genomic information
AI总结：
针对癌症基因组分析中样本有限导致的过拟合问题，提出融合MOE层与MOAE层、经33种癌症泛癌数据预训练的Gene-MOE框架，在生存分析和癌症分类任务上均优于现有最优模型。
摘要：
得益于深度学习的发展，各类基因组分析技术（如生存分析、肿瘤及其亚型分类、特定通路探究）已显著深化了我们对驱动癌症的生物学机制的理解。然而，患者样本数量有限导致的过拟合问题，给通过加深神经网络提升基因组分析精度带来了挑战。此外，稀疏门控混合专家（mixture of expert, MOE）、自注意力机制等新型方法能否提升基因组分析精度仍不明确。\n本文提出一种名为Gene-MOE的新型稀疏门控RNA-seq分析框架。该框架挖掘MOE层与本文提出的注意力混合专家（mixture of attention expert, MOAE）层的潜力，以提升分析精度；同时通过预训练整合33种不同癌症类型的泛癌信息，解决过拟合难题。\n我们在包含33种癌症类型的TCGA泛癌RNA-seq数据集上对Gene-MOE进行预训练，随后基于预训练的Gene-MOE开展癌症分类与生存分析实验。14种癌症类型的生存分析结果显示，Gene-MOE在12种癌症上的表现优于现有最优模型。通过细致的特征分析，我们发现Gene-MOE模型能够学习高维基因的丰富特征表示。分类结果表明，该分类模型在33种癌症分类任务上的总准确率达95.8%，相较现有最优模型取得了最佳性能。这些结果表明，Gene-MOE在癌症分类与生存分析领域具有强大的应用潜力。
英文摘要：
Benefiting from the advancements in deep learning, various genomic analytical techniques, such as survival analysis, classification of tumors and their subtypes, and exploration of specific pathways, have significantly enhanced our understanding of the biological mechanisms driving cancer. However, the overfitting issue, arising from the limited number of patient samples, poses a challenge in improving the accuracy of genome analysis by deepening the neural network. Furthermore, it remains uncertain whether novel approaches such as the sparsely gated mixture of expert (MOE) and self-attention mechanisms can improve the accuracy of genomic analysis. In this paper, we introduce a novel sparsely gated RNA-seq analysis framework called Gene-MOE. This framework exploits the potential of the MOE layers and the proposed mixture of attention expert (MOAE) layers to enhance the analysis accuracy. Additionally, it addresses overfitting challenges by integrating pan-cancer information from 33 distinct cancer types through pre-training.We pre-trained Gene-MOE on TCGA pan-cancer RNA-seq dataset with 33 cancer types. Subsequently, we conducted experiments involving cancer classification and survival analysis based on the pre-trained Gene-MOE. According to the survival analysis results on 14 cancer types, Gene-MOE outperformed state-of-the-art models on 12 cancer types. Through detailed feature analysis, we found that the Gene-MOE model could learn rich feature representations of high-dimensional genes. According to the classification results, the total accuracy of the classification model for 33 cancer classifications reached 95.8%, representing the best performance compared to state-of-the-art models. These results indicate that Gene-MOE holds strong potential for use in cancer classification and survival analysis.
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