College of Information Technology, Shanghai Ocean University, Hucheng Ring Road, Shanghai, China.
BMC Bioinformatics. 2021 Jan 6;22(1):13. doi: 10.1186/s12859-020-03949-w.
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.
通过特定化合物作用下的细胞扰动特征来预测癌症疾病的药物反应在个性化医疗中非常重要。在测试癌症药物反应的过程中，传统的实验方法受到成本和样本量的极大限制。目前，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具有挑战性的任务。
在这项研究中，我们引入了基于 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，并采用了基于随机森林和弹性网络的加权融合算法进行关键基因选择。然后，FEBPSO 算法被引入到 XGBoost 学习算法中，以预测药物诱导的细胞活力。将所提出的方法与一些新方法进行了比较，发现我们的模型取得了 0.83 的 Pearson 相关系数的良好结果。同时，我们在 NCI60 和 CCLE 数据集上完成了药物敏感性验证，进一步证明了我们方法的有效性。
结果表明，我们的方法有利于阐明疾病机制和探索新疗法，极大地推动了临床医学的进展。
