Machine learning-based identification of potentially novel non-alcoholic fatty liver disease biomarkers

Keyphrases

• Non-alcoholic Fatty Liver Disease (NAFLD) 100%
• Disease Biomarker 100%
• Machine Learning Based 100%
• Potential Biomarkers 60%
• C4BPA 60%
• Feature Selection 40%
• Preprocessing Techniques 40%
• Biological Interpretation 40%
• Complement Pathway 40%
• Random Forest Regression 40%
• Univariate Analysis 20%
• Lipid Metabolism 20%
• Chronic Liver Disease 20%
• Receiver Operating Characteristic Curve 20%
• Triglyceride Level 20%
• Path Analysis 20%
• Transcriptomics 20%
• Network Analysis 20%
• Differential Gene Expression Analysis 20%
• Boxplot 20%
• Machine Learning Approach 20%
• Downstream Analysis 20%
• Lipidomics 20%
• Feature Analysis 20%
• Correlation Plot 20%
• Correlation Heatmap 20%
• Variable Extraction 20%

Biochemistry, Genetics and Molecular Biology

• Random Forest 100%
• Feature Extraction 100%
• Downregulation 50%
• Lipid Metabolism 50%
• Triglyceride 50%
• Differential Gene Expression 50%
• Transcriptomics 50%
• Gene Expression Profiling 50%
• Lipidomics 50%