Abstract

Info

Author(s):: HUANG Pengjie1; 2; LIN Yong1; ZHANG Menghuan3; L?Lin1; LIU Zhenhao2; PEI Xiaoti1; XU Linfeng1; 2; XIE Lu2; 1. School of Medical Instrument and Food Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China 2. Shanghai Center for Bioinformation Technology, Shanghai 201203, China 3. Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences, Shanghai 200031, China

Keywords:: Keywords: ensemble learning tumor drug sensitivity prediction AdaBoost enrichment analysis

Abstract:: Abstract: The prediction of anti-tumor drug sensitivity is of great significance for personalized and precise medication. Herein a prediction model for RNA-seq gene expression and anti-cancer drug sensitivity data is established based on GDSC database through Boosting ensemble learning. A total of 183 drug sets are normalized, and gene feature dimensionality is reduced. Then, AdaBoost+SVM is used for modeling, and 10-fold cross validation for verifying. The experimental results show that the established prediction model has a high prediction accuracy. The AUC of 13, 108 and 174 drugs are greater than 0.95, 0.90 and 0.80, respectively. AdaBoost+SVM improves the comprehensive evaluation index of the overall drug set by about 4% and 2%, compared with the models based on a learner only and other ensemble models. Meanwhile, drug specificity is also discussed and the signal pathway of specific drug is verified through feature selection and enrichment analysis and the interpretability of the established model is confirmed from a biological perspective. In sum, the value of the established model in clinical medication guidance is proved in the study.