Prediction of drug-induced cell viability by SAE-XGBoost algorithm based on LINCS-L1000 perturbation signal.

Jiaxing LU; Ming CHEN; Yufang QIN; Xiaoqing YU

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Prediction of drug-induced cell viability by SAE-XGBoost algorithm based on LINCS-L1000 perturbation signal.

Author: Jiaxing LU ¹ ; Ming CHEN ¹ ; Yufang QIN ¹ ; Xiaoqing YU ²
Author Information

1. College of Information Technology, Shanghai Ocean University, Shanghai 201306, China.
2. School of Sciences, Shanghai Institute of Technology, Shanghai 201418, China.
Publication Type:Journal Article
Keywords: RW-XGBoost algorithm); cell viability; drug sensitivity; perturbation signals; stacked autoencoder deep neural network
MeSH: Algorithms; Antineoplastic Agents/pharmacology*; Cell Survival; Pharmaceutical Preparations
From: Chinese Journal of Biotechnology 2021;37(4):1346-1359
CountryChina
Language:Chinese
Abstract: Different cell lines have different perturbation signals in response to specific compounds, and it is important to predict cell viability based on these perturbation signals and to uncover the drug sensitivity hidden underneath the phenotype. We developed an SAE-XGBoost cell viability prediction algorithm based on the LINCS-L1000 perturbation signal. By matching and screening three major dataset, LINCS-L1000, CTRP and Achilles, a stacked autoencoder deep neural network was used to extract the gene information. These information were combined with the RW-XGBoost algorithm to predict the cell viability under drug induction, and then to complete drug sensitivity inference on the NCI60 and CCLE datasets. The model achieved good results compared to other methods with a Pearson correlation coefficient of 0.85. It was further validated on an independent dataset, corresponding to a Pearson correlation coefficient of 0.68. The results indicate that the proposed method can help discover novel and effective anti-cancer drugs for precision medicine.