用基因关联知识把生物表格数据转成图，提升小样本癌症分型准确率。
X2Graph for Cancer Subtyping Prediction on Biological Tabular Data
- 将表格列间关系转化为图结构，适配图神经网络
- 在三个癌症分型数据集上优于主流树模型和深度学习方法
- 适合缺乏标注数据的生物医学表格分析任务
尽管深度学习在文本、音频和图像数据上表现卓越，但在表格数据领域，尤其是医疗数据稀缺的情况下，其优势尚不明确。本文提出X2Graph，一种新型深度学习方法，在小型生物表格数据集上表现优异。该方法利用表中列之间的外部知识（如基因互作关系），将每个样本转换为图结构，从而可应用标准的消息传递图建模算法。X2Graph在三个癌症分型数据集上的表现显著优于现有的树模型与深度学习方法。
原文摘要 · Abstract (English)
Despite the transformative impact of deep learning on text, audio, and image datasets, its dominance in tabular data, especially in the medical domain where data are often scarce, remains less clear. In this paper, we propose X2Graph, a novel deep learning method that achieves strong performance on small biological tabular datasets. X2Graph leverages external knowledge about the relationships between table columns, such as gene interactions, to convert each sample into a graph structure. This transformation enables the application of standard message passing algorithms for graph modeling. Our X2Graph method demonstrates superior performance compared to existing tree-based and deep learning methods across three cancer subtyping datasets.
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