Prediction of serum anti-HSP27 antibody titers changes using a light gradient boosting machine (LightGBM) technique
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Previous studies have proposed that heat shock proteins 27 (HSP27) and its anti-HSP27 antibody titers may play a crucial role in several diseases including cardiovascular disease. However, available studies has been used simple analytical methods. This study aimed to determine the factors that associate serum anti-HSP27 antibody titers using ensemble machine learning methods and to demonstrate the magnitude and direction of the predictors using PFI and SHAP methods. The study employed Python 3 to apply various machine learning models, including LightGBM, CatBoost, XGBoost, AdaBoost, SVR, MLP, and MLR. The best models were selected using model evaluation metrics during the K-Fold cross-validation strategy. The LightGBM model (with RMSE: 0.1900 ± 0.0124; MAE: 0.1471 ± 0.0044; MAPE: 0.8027 ± 0.064 as the mean ± sd) and the SHAP method revealed that several factors, including pro-oxidant-antioxidant balance (PAB), physical activity level (PAL), platelet distribution width, mid-upper arm circumference, systolic blood pressure, age, red cell distribution width, waist-to-hip ratio, neutrophils to lymphocytes ratio, platelet count, serum glucose, serum cholesterol, red blood cells were associated with anti-HSP27, respectively. The study found that PAB and PAL were strongly associated with serum anti-HSP27 antibody titers, indicating a direct and indirect relationship, respectively. These findings can help improve our understanding of the factors that determine anti-HSP27 antibody titers and their potential role in disease development.
Originalsprog | Engelsk |
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Artikelnummer | 12775 |
Tidsskrift | Scientific Reports |
Vol/bind | 13 |
Udgave nummer | 1 |
Antal sider | 11 |
ISSN | 2045-2322 |
DOI | |
Status | Udgivet - 2023 |
Bibliografisk note
Funding Information:
The collection of clinical data was financially supported by Mashhad University of Medical Sciences.
Publisher Copyright:
© 2023, Springer Nature Limited.
ID: 362935802