We present two deep generative models based on Variational Autoencoders to
improve the accuracy of drug response prediction. Our models, Perturbation
Variational Autoencoder and its semi-supervised extension, Drug Response
Variational Autoencoder (Dr.VAE), learn latent representation of the underlying
gene states before and after drug application that depend on: (i) drug-induced
biological change of each gene and (ii) overall treatment response outcome. Our
VAE-based models outperform the current published benchmarks in the field by
anywhere from 3 to 11% AUROC and 2 to 30% AUPR. In addition, we found that
better reconstruction accuracy does not necessarily lead to improvement in
classification accuracy and that jointly trained models perform better than
models that minimize reconstruction error independently.

该研究基于变分自动编码器提出了两种深度生成模型，学习基因状态的潜在表示，以提高药物响应预测的准确性。结果表明，该模型可在 AUROC 和 AUPR 方面超过先前发表的研究，同时发现更好的重构准确性并不一定导致分类准确性的提高，联合训练模型表现更好。