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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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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