We study the challenges of applying deep learning to gene expression data. We find experimentally that there exists non-linear signal in the data, however is it not discovered automatically given the noise and low numbers of samples used in most research. We discuss how gene interaction graphs (same pathway, protein-protein, co-expression, or research paper text association) can be used to impose a bias on a deep model similar to the spatial bias imposed by convolutions on an image. We explore the usage of Graph Convolutional Neural Networks coupled with dropout and gene embeddings to utilize the graph information. We find this approach provides an advantage for particular tasks in a low data regime but is very dependent on the quality of the graph used. We conclude that more work should be done in this direction. We design experiments that show why existing methods fail to capture signal that is present in the data when features are added which clearly isolates the problem that needs to be addressed.

研究使用深度学习应用到基因表达数据的挑战，发现存在非线性信号但需要使用基因交互图结合图卷积神经网络和基因嵌入以利用图信息来克服数据量不足的问题，但模型表现依赖于图的质量，并发现更多工作需要在这个方向上展开。

利用基因相互作用图进行基因表达卷积