[Prediction of drug-induced cell viability by SAE-XGBoost algorithm based on LINCS-L1000 perturbation signal].
[Prediction of drug-induced cell viability by SAE-XGBoost algorithm based on LINCS-L1000 perturbation signal].
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DOI：
10.13345/j.cjb.200450
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发表时间：
2021-04-25
期刊：
影响因子：
--
通讯作者：
Yu, Xiaoqing
中科院分区：
文献类型：
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作者：
Lu, Jiaxing;Chen, Ming;Yu, Xiaoqing
不同的细胞系对特定化合物有不同的干扰信号，根据这些干扰信号预测细胞活力并揭示隐藏在表型下的药物敏感性是很重要的。我们开发了一种基于LINCS-L1000扰动信号的SAE-XGBoost细胞活力预测算法。通过匹配和筛选LINCS-L1000、CTRP和Achilles三个主要数据集，使用堆叠式自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，然后在NCI 60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，Pearson相关系数为0.85。在独立数据集上进一步验证，对应于0.68的Pearson相关系数。结果表明，所提出的方法可以帮助发现新的和有效的抗癌药物的精准医学。
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.