Prediction of Anti-Cancer Drug Combinations Synergy Based on Variance Ranking and Deep Neural Network
Identifying novel synergistic combinations to a specific cancer is significant for improving cancer treatment.Based on variance ranking and deep neural network,four types of VarDNN models were built,and five-fold nested cross validation and leave-one-out method were employed to predict anti-cancer drug synergy comprehensively.The result shows that the prediction performance of VarDNN is superior to the state-of-the-art models.Most im-portantly,the synergy prediction of"new cell line-new and old drug pair",which has not yet been studied,is consistent with some known conclusions.Furthermore,VarDNN could identify some biomarkers closely related to the occurrence and development of cancers,and provide a theoretical reference for the screening of anti-cancer drug combinations in some extent.
anti-cancer drug combinationvariance rankingdeep neural networksynergy predictionbiomarker
NETL
NSTL
万方数据
