کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
6866309 | 678171 | 2014 | 7 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Interpretable prediction of non-genotoxic hepatocarcinogenic chemicals
ترجمه فارسی عنوان
پیش بینی قابل تفسیر مواد شیمیایی غیر پتاسیم زنجیره ای غیر ژنوتیک
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کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
هوش مصنوعی
چکیده انگلیسی
The assessment of non-genotoxic hepatocarcinogenicity of chemicals relies on time-consuming rodent bioassays. The development of alternative methods for non-genotoxic hepatocarcinogenicity could help the identification of potential hepatocarcinogenic chemicals. This study evaluated four types of features for the interpretable prediction of non-genotoxic hepatocarcinogenic chemicals including chemical-chemical interactions (CCI), chemical-protein interactions (CPI), chemical descriptors (QSAR) and gene expression profiles (TGx). Based on the results of decision tree classifiers, the CPI-based features perform best with independent test accuracies of 90% and 86% for interaction scores from combined scores and databases, respectively. Informative features were identified and analyzed to give insights into the non-genotoxic hepatocarcinogenicity of chemicals. The difference between CPI scores and gene expression profiles for the identified important proteins shows that CPI could play more important roles in non-genotoxic hepatocarcinogenicity.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Neurocomputing - Volume 145, 5 December 2014, Pages 68-74
Journal: Neurocomputing - Volume 145, 5 December 2014, Pages 68-74
نویسندگان
Chun-Wei Tung, Jhao-Liang Jheng,