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Drug-induced cell viability prediction from LINCS-L1000 through WRFEN-XGBoost algorithm
Jiaxing Lu1, Ming Chen2, Yufang Qin3
1College of Information Technology, Shanghai Ocean University, Hucheng Ring Road, Shanghai, China.
BMC Bioinformatics
|January 7, 2021
Summary
This study introduces a novel machine learning algorithm for predicting cancer drug response using gene expression data. The WRFEN-XGBoost model accurately predicts cell viability, advancing personalized cancer medicine.
Area of Science:
- Computational biology
- Genomics
- Pharmacogenomics
Background:
- Predicting cancer drug response is crucial for personalized medicine.
- Traditional methods are limited by cost and sample size.
- Large gene expression datasets present opportunities for machine learning in drug sensitivity prediction.
Purpose of the Study:
- To develop a machine learning algorithm for predicting cancer cell viability and drug response.
- To leverage large-scale gene expression data for improved prediction accuracy.
Main Methods:
- Developed the WRFEN-XGBoost algorithm using LINCS-L1000 cell perturbation signatures.
- Integrated LINCS-L1000, CTRP, and Achilles datasets.
- Employed a weighted fusion algorithm for key gene selection and FEBPSO-XGBoost for cell viability prediction.
Main Results:
- The WRFEN-XGBoost model achieved a Pearson correlation of 0.83 in cell viability prediction.
- Validated drug sensitivity on NCI60 and CCLE datasets, confirming the method's effectiveness.
- Demonstrated superior performance compared to existing methods.
Conclusions:
- The developed method aids in understanding disease mechanisms and discovering novel therapies.
- This approach significantly contributes to the advancement of clinical medicine and personalized treatment strategies.
