Circular-SWAT for deep learning based diagnostic classification of Alzheimer's disease: application to metabolome data

Keyphrases

• Alzheimer's Disease 100%
• Deep Learning Methods 100%
• Metabolomics 100%
• Diagnostic Classification 100%
• Association Test 100%
• Sliding Window 100%
• Circular Sliding 100%
• Classification Accuracy 42%
• Lipids 28%
• Feature Selection 28%
• Deep Learning 14%
• Neurodegenerative Diseases 14%
• Therapeutic Potential 14%
• Cognitively Normal Older Adults 14%
• Lipidomics 14%
• Alzheimer's Disease Neuroimaging Initiative 14%
• Targeted Therapeutics 14%
• Lipid Biomarkers 14%
• Metabolomics Data 14%
• Omics Data 14%
• Testing Method 14%
• Model Validation 14%
• Training Model 14%
• Random Forest 14%
• Weighted Correlation Network Analysis (WGCNA) 14%
• Feature Correlation 14%
• Convolutional Neural Network 14%
• Scientific Domain 14%
• Feature Classification 14%
• Three-step Approach 14%
• Feature Correlation Analysis 14%

Biochemistry, Genetics and Molecular Biology

• Lipid 100%
• Metabolome 100%
• Feature Extraction 66%
• Lipidomics 33%
• Metabolomics 33%
• Random Forest 33%
• Tamsulosin 33%

Neuroscience

• Alzheimer's Disease 100%
• Metabolome 100%
• Neurodegenerative Disorder 12%
• Neural Network 12%
• Face 12%
• Tamsulosin 12%