Lu Jiaxing, Chen Ming, Qin Yufang, Yu Xiaoqing
College of Information Technology, Shanghai Ocean University, Shanghai 201306, China.
School of Sciences, Shanghai Institute of Technology, Shanghai 201418, China.
Sheng Wu Gong Cheng Xue Bao. 2021 Apr 25;37(4):1346-1359. doi: 10.13345/j.cjb.200450.
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.
不同的细胞系对特定化合物有不同的扰动信号，基于这些扰动信号预测细胞活力并揭示表型背后隐藏的药物敏感性很重要。我们基于LINCS-L1000扰动信号开发了一种SAE-XGBoost细胞活力预测算法。通过匹配和筛选三个主要数据集LINCS-L1000、CTRP和Achilles，使用堆叠自动编码器深度神经网络提取基因信息。将这些信息与RW-XGBoost算法相结合，预测药物诱导下的细胞活力，进而在NCI60和CCLE数据集上完成药物敏感性推断。与其他方法相比，该模型取得了良好的结果，皮尔逊相关系数为0.85。在独立数据集上进一步验证，对应皮尔逊相关系数为0.68。结果表明，所提出的方法有助于发现用于精准医学的新型有效抗癌药物。
