Motivation

Biological pathway is important curated knowledge of biological processes. Thus, cancer subtype classification based on pathways will be very useful to understand differences in biological mechanisms among cancer subtypes. However, pathways include only a fraction of the entire gene set, only 1/3 of human genes in KEGG, and pathways are fragmented. For this reason, there are few computational methods to use pathways for cancer subtype classification.

Results

We present an explainable deep learning model with attention mechanism and network propagation for cancer subtype classification. Each pathway is modeled by a graph convolutional network. then, a multi-attention based ensemble model combines several hundreds of pathways in an explainable manner. Lastly, network propagation on pathway-gene network explains why gene expression profiles in subtypes are different. In experiments with five TCGA cancer data sets, our method achieved very good classification accuracies and, additionally, identified subtype-specific pathways and biological functions.

Supplementary information

Supplementary data are available at Bioinformatics online.

中文翻译：

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通过途径的关注和传播对癌症亚型进行分类和建模

动机

生物途径是重要的生物过程知识。因此，基于途径的癌症亚型分类对于理解癌症亚型之间生物学机制的差异将非常有用。但是，途径仅占整个基因组的一小部分，在KEGG中仅占人类基因的1/3，并且途径是片段化的。因此，很少有计算方法可将途径用于癌症亚型分类。

结果

我们提出了一种具有注意力机制和网络传播的可解释性深度学习模型，用于癌症亚型分类。每个路径都由图卷积网络建模。然后，基于多注意的集成模型以一种可解释的方式组合了数百个路径。最后，通路基因网络上的网络传播解释了为什么亚型中的基因表达谱不同。在使用五个TCGA癌症数据集进行的实验中，我们的方法获得了很好的分类精度，此外，还确定了亚型特异性途径和生物学功能。

补充资料

补充数据可从生物信息学在线获得。