论文标题
抗癌药物反应预测的变分自动编码器
Variational Autoencoder for Anti-Cancer Drug Response Prediction
论文作者
论文摘要
癌症是人类死亡的主要原因，但是发现药物和调整癌症疗法是昂贵且耗时的。我们试图使用变分自动编码器（VAE）和多层感知器（MLP）促进发现新药和治疗策略，以预测抗癌药物反应。我们的模型将癌细胞系和抗癌药物分子数据的输入基因表达数据（分别是普通的VAE）模型编码这些数据，该数据分别是普通的VAE模型，并且分别是整流的交界树变量自动码模型（{\ sc jtvae}模型）。多层感知器处理这些编码的功能以产生最终预测。我们的测试表明，我们的系统在预测乳腺癌细胞系的药物反应和平均$ r^{2} = 0.845 $的平均药物反应方面达到了高平均测定系数（$ r^{2} = 0.83 $）。此外，我们表明我们的模型可以生成以前不用于特定癌细胞系的有效药物化合物。
Cancer is a primary cause of human death, but discovering drugs and tailoring cancer therapies are expensive and time-consuming. We seek to facilitate the discovery of new drugs and treatment strategies for cancer using variational autoencoders (VAEs) and multi-layer perceptrons (MLPs) to predict anti-cancer drug responses. Our model takes as input gene expression data of cancer cell lines and anti-cancer drug molecular data and encodes these data with our {\sc {GeneVae}} model, which is an ordinary VAE model, and a rectified junction tree variational autoencoder ({\sc JTVae}) model, respectively. A multi-layer perceptron processes these encoded features to produce a final prediction. Our tests show our system attains a high average coefficient of determination ($R^{2} = 0.83$) in predicting drug responses for breast cancer cell lines and an average $R^{2} = 0.845$ for pan-cancer cell lines. Additionally, we show that our model can generates effective drug compounds not previously used for specific cancer cell lines.
