Background
Different cell lines have different perturbation signatures under the action of specific compounds. Based on these perturbation signatures, it is very important to predict the cell viability and explore the biological molecular mechanism of action hidden under the phenotype. 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.
Results
In this study, we developed the WRFEN-XGBoost cell viability prediction algorithm based on the LINCS-L1000 perturbation signatures. We integrateed 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, we combined the FEBPSO-XGBoost algorithm to predict the cell viability under the drug induction. Compared to other methods, 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.
Conclusions
Our results indicated that the proposed method could provide help to find the effective anti-cancer drugs and contributed to the medical research.