Identification of early liver toxicity gene biomarkers using comparative supervised machine learning

Keyphrases

• Supervised Machine Learning 100%
• Liver Toxicity 100%
• Gene Signature 100%
• Gene Biomarkers 100%
• Liver Necrosis 100%
• High Selectivity 50%
• Predictive Models 50%
• High Specificity 50%
• Metabolism 50%
• Microarray 50%
• Supervised Methods 50%
• Quality Control 50%
• Transcriptional Regulation 50%
• Gene number 50%
• Cost Reduction 50%
• Rat Liver 50%
• Detoxification 50%
• Reduced Time 50%
• Feature Selection Methods 50%
• Feature Selection 50%
• Agrochemicals 50%
• Validation Data 50%
• Statistical Accuracy 50%
• Early-life Exposure 50%
• Liver Carcinogenesis 50%
• Liver Cancer 50%
• Regulatory Approval 50%
• Toxicity Testing 50%
• Biomarker Genes 50%
• Regulatory Toxicity Testing 50%
• Predictive Testing 50%
• TG-GATEs 50%

Pharmacology, Toxicology and Pharmaceutical Science

• Biological Marker 100%
• Hepatotoxicity 100%
• Liver Necrosis 66%
• Drug 33%
• Detoxication 33%
• Liver Cancer 33%
• Liver Carcinogenesis 33%
• Agricultural Chemical 33%

Biochemistry, Genetics and Molecular Biology

• Gene Signature 100%
• Supervised Machine Learning 100%
• Metabolic Pathway 50%
• Carcinogenesis 50%
• Microarrays 50%
• Quality Control 50%