Predicting the drug response of the cancer diseases through the cellular perturbation signatures under the action of specific compounds is very important in personalized medicine. In the process of testing drug responses to the cancer, traditional experimental methods have been greatly hampered by the cost and sample size. At present, the public availability of large amounts of gene expression data makes it a challenging task to use machine learning methods to predict the drug sensitivity. In this study, we introduced the WRFEN-XGBoost cell viability prediction algorithm based on LINCS-L1000 cell signatures. We integrated the LINCS-L1000, CTRP and Achilles datasets and adopted a weighted fusion algorithm based on random forest and elastic net for key gene selection. Then the FEBPSO algorithm was introduced into XGBoost learning algorithm to predict the cell viability induced by the drugs. The proposed method was compared with some new methods, and it was found that our model achieved good results with 0.83 Pearson correlation. At the same time, we completed the drug sensitivity validation on the NCI60 and CCLE datasets, which further demonstrated the effectiveness of our method. The results showed that our method was conducive to the elucidation of disease mechanisms and the exploration of new therapies, which greatly promoted the progress of clinical medicine.

中文翻译：

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通过WRFEN-XGBoost算法从LINCS-L1000预测药物诱导的细胞生存力

在特定化合物的作用下，通过细胞扰动信号来预测癌症疾病的药物反应在个性化医学中非常重要。在测试药物对癌症的反应过程中，传统的实验方法受到成本和样本量的极大限制。当前，大量基因表达数据的公开可用性使得使用机器学习方法预测药物敏感性成为一项艰巨的任务。在这项研究中，我们介绍了基于LINCS-L1000细胞特征的WRFEN-XGBoost细胞活力预测算法。我们整合了LINCS-L1000，CTRP和Achilles数据集，并采用基于随机森林和弹性网的加权融合算法选择关键基因。然后将FEBPSO算法引入XGBoost学习算法中，以预测药物诱导的细胞活力。将提出的方法与一些新方法进行比较，发现我们的模型在0.83的Pearson相关性下取得了良好的效果。同时，我们在NCI60和CCLE数据集上完成了药物敏感性验证，这进一步证明了我们方法的有效性。结果表明，该方法有利于阐明疾病的机理和探索新的疗法，极大地促进了临床医学的发展。我们完成了对NCI60和CCLE数据集的药物敏感性验证，这进一步证明了我们方法的有效性。结果表明，该方法有利于阐明疾病的机理和探索新的疗法，极大地促进了临床医学的发展。我们完成了对NCI60和CCLE数据集的药物敏感性验证，这进一步证明了我们方法的有效性。结果表明，该方法有利于阐明疾病的机理和探索新的疗法，极大地促进了临床医学的发展。