Date of Publication:2024-01-01

Journal:Computers in Biology and Medicine

Volume:173

ISSN No.:0010-4825

Key Words:acetaldehyde; alanine aminotransferase; alanine aminotransferase blood level; alcohol dehydrogenase; alkaline phosphatase; alkaline phosphatase blood level; analgesic agent; animal experiment; antiinflammatory agent; Article; artificial intelligence; artificial neural network; aspartic acid; Chinese drug; cholesterol blood level; controlled study; convolutional neural network; Convolutional neural networks; creatine kinase; creatinine; creatinine blood level; cytochrome; Data augmentation; decision tree; deep learning; deep neural network; drug classification; drug megadose; feature extraction; feature selection; Feature Selection; Features selection; feed forward neural network; Forecasting; glucose; glucose blood level; glutathione; glutathione peroxidase; glutathione transferase; glycoprotein; graph neural network; gunqile 7; Gunqile-7; hepatitis A virus cellular receptor 1; high density lipoprotein; immunoglobulin enhancer binding protein; interleukin 1; interleukin 6; k nearest neighbor; lactate dehydrogenase; Learning systems; learning theory; liver toxicity; long short term memory network; low density lipoprotein cholesterol; low drug dose; machine learning; Machine Learning; Mongolian medicine; Mongolians; nephrotoxicity; Neural Networks, Computer; nitrogen; nonhuman; prediction; proportional hazards model; protein blood level; random forest; rat; recursive feature elimination; Small samples; superoxide dismutase; support vector machine; Support Vector Machine; Support vector machines; Toxicity classification; Toxicity predictions; toxicity testing; Transfer learning; Transfer learning methods; transfer of learning; triacylglycerol; tumor necrosis factor; unclassified drug; urea; urea nitrogen blood level