Prediction of serum anti-HSP27 antibody titers changes using a light gradient boosting machine (LightGBM) technique
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Prediction of serum anti-HSP27 antibody titers changes using a light gradient boosting machine (LightGBM) technique. / Talkhi, Nasrin; Nooghabi, Mehdi Jabbari; Esmaily, Habibollah; Maleki, Saba; Hajipoor, Mojtaba; Ferns, Gordon A.; Ghayour-Mobarhan, Majid.
I: Scientific Reports, Bind 13, Nr. 1, 12775, 2023.Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › fagfællebedømt
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TY - JOUR
T1 - Prediction of serum anti-HSP27 antibody titers changes using a light gradient boosting machine (LightGBM) technique
AU - Talkhi, Nasrin
AU - Nooghabi, Mehdi Jabbari
AU - Esmaily, Habibollah
AU - Maleki, Saba
AU - Hajipoor, Mojtaba
AU - Ferns, Gordon A.
AU - Ghayour-Mobarhan, Majid
N1 - Publisher Copyright: © 2023, Springer Nature Limited.
PY - 2023
Y1 - 2023
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85166783034&partnerID=8YFLogxK
U2 - 10.1038/s41598-023-39724-z
DO - 10.1038/s41598-023-39724-z
M3 - Journal article
C2 - 37550399
AN - SCOPUS:85166783034
VL - 13
JO - Scientific Reports
JF - Scientific Reports
SN - 2045-2322
IS - 1
M1 - 12775
ER -
ID: 362935802