Hierarchical-TGDR: Combining biological hierarchy with a regularization method for multi-class classification of lung cancer samples via high-throughput gene-expression data

Keyphrases

• Lung Cancer 100%
• High-throughput 100%
• Gene Expression Data 100%
• Regularization Method 100%
• Multi-class Classification 100%
• Biological Hierarchy 100%
• Adenocarcinoma Cells 50%
• Lung Carcinoma 50%
• Tumor 50%
• Microarray Data 50%
• Squamous Cell Carcinoma (SqCC) 50%
• Clinical Stage I 50%
• Hierarchical Structure 50%
• Pathogenic Mechanism 50%
• Omics Data 50%
• Small Sets 50%
• Classification Problem 50%
• Transcriptomics 50%
• Performance Prediction 50%
• Classification Algorithms 50%
• Existing Structures 50%
• Selected Features 50%
• Diagnostic Signature 50%
• Sbv IMPROVER 50%
• Discriminative Performance 50%
• Gradient Descent 50%
• Regularization Algorithm 50%
• Threshold Gradient 50%
• Invasion Pathways 50%
• Data Reducing 50%
• Data Hierarchy 50%

Computer Science

• High Throughput 100%
• Gene Expression Data 100%
• Regularization 100%
• Regularization Method 100%
• Multiclass Classification 100%
• Biological Hierarchy 100%
• Multi Class Classification 100%
• Classification Algorithm 50%
• Gradient Descent 50%
• Microarray Data 50%
• Predictive Performance 50%
• Threshold Algorithm 50%