Gene Expression Profile Prediction under Drug Action Based on Generative Adversarial Networks

Gene expression profiles play a significant role in drug research. If the gene expression profile under the action of drugs can be obtained quickly, such as through computational methods, the analysis of the relationship between the drug and the disease will become more comprehensive. The efficiency can be improved and costs can be reduced while exploring the effect of the drug. We developed an algorithm (ppc-GAN, predict-profile-conditional Generative Adversarial Networks) for predicting gene expression profiles for drug effects, which can efficiently and accurately obtain the gene expression profiles after drug administration. Compared with traditional algorithms, ppc-GAN does not require more prior knowledge. Therefore, the final prediction result will not be affected by the preference of prior knowledge. Our ppc-GAN mainly includes two parts—an autoencoder and a generative adversarial network (GAN). We trained the autoencoder through all gene expression profile data in the LINCS database and then merged the trained autoencoder into the GAN for data compression and decompression. Besides, we chose bortezomib as the case drug. Our results show that our model is flexible and has high representative power. Furthermore, the state of the gene expression profile after using the drug can be estimated by the deep learning models.