Drug-induced cell viability prediction from LINCS-L1000 through WRFEN-XGBoost algorithm.

ABSTRACT:

Background

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.

Results

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

SUBMITTER: Lu J 

PROVIDER: S-EPMC7788947 | biostudies-literature | 2021 Jan

REPOSITORIES: biostudies-literature