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Research on Drug Response Prediction Model Based on Big Data

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Advances in Artificial Intelligence and Security (ICAIS 2021)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1422))

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Abstract

Personalized medicine, also known as precision medicine, refers to a medical model of providing the best treatment plan for a patient according to his or her personal genomic information. The research and practice of personalized medicine have become a hot topic in current medical research, and predicting the response of cell lines to specific drugs is one of the core problems. Using computer algorithms to predict the responses of cell lines to drugs based on huge amounts of existing omics information is currently one focus of bioinformatics. A variety of predictive methods have been proposed. The paper introduces the baseline analysis data, surveys some classical prediction methods and models, and details on the application of matrix decomposition, heterozygous network and deep learning at the drug response prediction. At last, some existing problems and future development trend and prospects are discussed.

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Acknowledgement

This work has been supported by the National Natural Science Foundation of China (Grant No. 61772197).

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Authors and Affiliations

  1. College of Information Science and Engineering, Hunan Normal University, Changsha, 410081, China

    Guijin Li & Minzhu Xie

Authors
  1. Guijin Li
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  2. Minzhu Xie
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Corresponding author

Correspondence to Minzhu Xie .

Editor information

Editors and Affiliations

  1. Nanjing University of Information Science and Technology, Nanjing, China

    Xingming Sun

  2. Nanjing University of Information Science and Technology, Nanjing, China

    Xiaorui Zhang

  3. Jinan University, Guangzhou, China

    Zhihua Xia

  4. Purdue University, West Lafayette, IN, USA

    Elisa Bertino

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Li, G., Xie, M. (2021). Research on Drug Response Prediction Model Based on Big Data. In: Sun, X., Zhang, X., Xia, Z., Bertino, E. (eds) Advances in Artificial Intelligence and Security. ICAIS 2021. Communications in Computer and Information Science, vol 1422. Springer, Cham. https://doi.org/10.1007/978-3-030-78615-1_46

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  • DOI: https://doi.org/10.1007/978-3-030-78615-1_46

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-78614-4

  • Online ISBN: 978-3-030-78615-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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