Optimization and prediction of ERα activity screening of anti-breast cancer candidate based on machine learning algorithm

Jiewang Liu; Anjun Song

doi:10.1117/12.2634705

4 May 2022 Optimization and prediction of ERα activity screening of anti-breast cancer candidate based on machine learning algorithm

Jiewang Liu, Anjun Song

Author Affiliations +

Proceedings Volume 12172, International Conference on Electronic Information Engineering and Computer Communication (EIECC 2021); 1217216 (2022) https://doi.org/10.1117/12.2634705
Event: International Conference on Electronic Information Engineering and Computer Communication (EIECC 2021), 2021, Nanchang, China

Abstract

Breast cancer is currently one of the most common and fatal cancers in the world. Studies have shown that ERα is an important target for the treatment of breast cancer, and how to effectively develop compounds that can antagonize ERα activity and have a good ADMET properties is a key factor in the development of breast cancer and related drugs. Around the problem of feature reduction and screening of sample data, based on the highly nonlinearity and strong coupling between the data samples, this paper innovatively uses the hierarchical analysis method (AHP) to combine feature selection with feature dimension reduction, the correlation of pIC50 and the remaining 793 variables was studied using Pearson correlation, Spearman correlation, Kendall correlation, recursive feature elimination, and random forest. Different methods were weighted by hierarchical analysis, and the highly correlated variables were removed by the independence test. The main variables such as MDEC-23, MLogP, LipoaffinityIndex were finally obtained. Centering on the prediction and optimization of ERα as an anti-breast cancer candidate, this paper proposed three quantitative prediction models of ERα biological activity (Gaussian process regression and XGBoost). We experimentally analyzed the prediction effect of the three models, finally determined the XGBoost distributed prediction model with a goodness of fit of 91%, and introduced various evaluation indicators to verify the accuracy and scientificity of the proposed prediction model.

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Jiewang Liu and Anjun Song "Optimization and prediction of ERα activity screening of anti-breast cancer candidate based on machine learning algorithm", Proc. SPIE 12172, International Conference on Electronic Information Engineering and Computer Communication (EIECC 2021), 1217216 (4 May 2022); https://doi.org/10.1117/12.2634705

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KEYWORDS

Data modeling

Breast cancer

Biological research

Cancer

Feature selection

Dimension reduction

Machine learning

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