Abstract
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.
Original language | English |
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Pages (from-to) | 68-74 |
Number of pages | 7 |
Journal | Neurocomputing |
Volume | 145 |
DOIs | |
Publication status | Published - Dec 5 2014 |
Externally published | Yes |
Keywords
- Chemical-chemical interaction
- Chemical-protein interaction
- Decision tree
- Non-genotoxic hepatocarcinogenicity
- Quantitative structure-activity relationship
- Toxicogenomics
ASJC Scopus subject areas
- Computer Science Applications
- Cognitive Neuroscience
- Artificial Intelligence