التفاصيل البيبلوغرافية
| العنوان: |
Recognition of specific types of drug-induced liver injury using random forest algorithm: the importance of individual serum bile acid level. |
| Alternate Title: |
基于大鼠血清胆酸盐水平构建药物性肝损伤随机森林分类模型. (Chinese) |
| المؤلفون: |
Yongwen Jin, Lili Xi, Yuhui Wei, Xinan Wu |
| المصدر: |
Journal of Chinese Pharmaceutical Sciences; Sep2022, Vol. 31 Issue 9, p677-688, 12p |
| مصطلحات موضوعية: |
RANDOM forest algorithms, BILE acids, DRUG side effects, LIVER injuries, ENTEROHEPATIC circulation, BILE salts |
| Abstract (English): |
Drug-induced liver injury (DILI) is associated with an imbalance in the homeostasis of bile salts (BAs). Howeve r, a clear connection between BAs and different types of DILI remains to be established. In the present study, random forest (RF) machine learning prediction systems were deployed with 17 individual BAs for categorizing DILI. BAs were analyzed via LC-MS/MS in the serum using the model of seven known hepatotoxins (isoniazid, acetaminophen, bendazac, 17α-ethinylestradiol, 1-naphthylisothiocyanate, tetracycline, and ticlopidine), which caused cholestasis, steatosis, and necrosis in rats. The RF model was validated via leave-one-out cross-validation. The importance of each individual BA with respect to prediction ability was determined. The RF model achieved the best prediction performance, producing accuracy values of 0.98, 0.97, and 1.00 for leave-one-out cross-validation, the training set, and the external test set, respectively. The order of descriptor‟s importance was obtained, which was TUDCA > GUDCA > TCA > THDCA. The specificity values for necrosis, cholestasis, and steatosis were 0.94, 1.00, and 1.00, respectively. The results indicated the potential value of individual BA level in serum for categorizing DILI. The RF model in the present work was an inexpensive and readily available tool for categorizing DILI. [ABSTRACT FROM AUTHOR] |
| Abstract (Chinese): |
药物性肝损伤(DILI)与体内胆酸盐(BAs)稳态的失衡有关。然而, 胆酸盐与不同类型DILI的关联性尚不明确。本研 究以17种胆酸盐为变量建立一种基于随机森林法(RF)的DILI分型评估模型, 探讨其在预测DILI分型中的价值。大鼠分别 给予7种造模药物(异烟肼、对乙酰氨基酚、苄达赖氨酸、17α-炔雌醇、1-萘异硫氰酸酯、四环素和噻氯匹定)诱导肝细胞 坏死型肝损、胆汁淤积型肝损伤和脂肪变性型肝损伤; 采用LC-MS/MS方法测定大鼠血清中17种胆酸盐含量; 建立RF分类 模型; 通过抽一法(LOO)交互检验对所有BAs的预测能力权重进行重要性排序。结果表明RF模型具有较好的预测性能, LOO法验证、训练集和外部测试集的准确度分别为0.98、0.97和1.00; BAs的预测能力权重重要性排序为TUDCA > GUDCA > TCA > THDCA; RF模型对肝细胞坏死型、胆汁淤积型和脂肪变性型预测的特异性分别为0.94、1.00和1.00。以上结果提示 不同类型DILI血清中BAs含量变化不同, 基于BAs含量变化建立的RF模型对DILI进行分类准确度较高. [ABSTRACT FROM AUTHOR] |
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| قاعدة البيانات: |
Complementary Index |
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