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Imputation of Compound Property Assay Data Using a Gene Expression Programming-Based Method

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Applied Intelligence (ICAI 2023)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 2014))

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Abstract

Compound property assays are an important part of drug development, but incomplete data may occur for a variety of reasons. To deal with these incomplete data and improve the success rate of drug development, researchers often need to effectively impute the missing data. Therefore, this paper proposes a gene expression programming-based method, called GEP-CPI, for imputing missing compound property assay data. In GEP-CPI, the missing data imputation model is expressed by the parse tree of a chromosome, and then the optimal missing data imputation model is mined by iterative evolution of the chromosome population. Experimental results on three compound property assay related datasets demonstrates that the proposed method generally outperforms the state-of-the-art methods in imputing missing data of compound property assays.

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Acknowledgements

This work was supported in part by the National Natural Science Foundation of China (#62262044), and Natural Science Foundation of Guangxi Province (#2023GXNSFAA026027), the Project of Guangxi Chinese medicine multidisciplinary crossover innovation team (#GZKJ2311).

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Authors and Affiliations

  1. Key Lab of Scientific Computing and Intelligent Information Processing, Nanning Normal University, Nanning, 5300001, Guangxi, China

    Hongliang Zhou, Yanmei Lin, Nan Chen & Yuzhong Peng

  2. Guangxi Academy of Sciences, Nanning, 530007, Guangxi, China

    Yuzhong Peng

Authors
  1. Hongliang Zhou
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  2. Yanmei Lin
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  3. Nan Chen
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  4. Yuzhong Peng
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Corresponding authors

Correspondence to Yanmei Lin or Yuzhong Peng .

Editor information

Editors and Affiliations

  1. Eastern Institute of Technology, Zhejiang, China

    De-Shuang Huang

  2. University of Wollongong, North Wollongong, NSW, Australia

    Prashan Premaratne

  3. Guangxi Academy of Sciences, Guangxi, China

    Changan Yuan

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© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Zhou, H., Lin, Y., Chen, N., Peng, Y. (2024). Imputation of Compound Property Assay Data Using a Gene Expression Programming-Based Method. In: Huang, DS., Premaratne, P., Yuan, C. (eds) Applied Intelligence. ICAI 2023. Communications in Computer and Information Science, vol 2014. Springer, Singapore. https://doi.org/10.1007/978-981-97-0903-8_13

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  • DOI: https://doi.org/10.1007/978-981-97-0903-8_13

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-97-0902-1

  • Online ISBN: 978-981-97-0903-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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