Using Deep Neural Network to Predict Drug Sensitivity of Cancer Cell Lines

  • Yake Wang
  • Min LiEmail author
  • Ruiqing Zheng
  • Xinghua Shi
  • Yaohang Li
  • Fangxiang Wu
  • Jianxin Wang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10955)


High-throughput screening technology has provided a large amount of drug sensitivity data for hundreds of compounds on cancer cell lines. In this study, we have developed a deep learning architecture based on these data to improve the performance of drug sensitivity prediction. We used a five-layer deep neural network, named as DeepPredictor, that integrated both genomic features of cell lines and chemical information of compounds to predict the half maximal inhibitory concentration on the Cancer Cell Line Encyclopedia (CCLE) dataset. We demonstrated the performance of our deep model using 10-fold cross-validations and leave-one-out strategies and showed that our model outperformed existing approaches.


Cancer cell lines Drug sensitivity DeepPredictor Deep learning Predictive models 



We would like to thank Isidro Cortés Ciriano at Department of Biomedical Informatics, Harvard Medical School for providing the research data and discussing with us during research. This work is supported by the National Science Fund for Excellent Young Scholars under Grant No. 61622213, the National Natural Science Foundation of China under grant No. 61772552, and the Fundamental Research Funds for the Central Universities of Central South University under grant No. 2018zzts560 and No. 2018zzts028.


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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Yake Wang
    • 1
  • Min Li
    • 1
    Email author
  • Ruiqing Zheng
    • 1
  • Xinghua Shi
    • 2
  • Yaohang Li
    • 3
  • Fangxiang Wu
    • 4
  • Jianxin Wang
    • 1
  1. 1.School of Information Science and EngineeringCentral South UniversityChangshaChina
  2. 2.Department of Bioinformatics and Genomics, College of Computing and InformaticsUniversity of North Carolina at CharlotteCharlotteUSA
  3. 3.Department of Computer ScienceOld Dominion UniversityNorfolkUSA
  4. 4.Division of Biomedical EngineeringUniversity of SaskatchewanSaskatoonCanada

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