Multi-class protein fold classification using a new ensemble machine learning approach.

Keyphrases

• Base Classifier 50%
• Bioinformatics 100%
• Biological Databases 50%
• Class Imbalance 50%
• Class-imbalanced Data 50%
• Different Bases 50%
• Ensemble Machine Learning 100%
• Evolutionary Relationship 50%
• Functional Relationship 50%
• High-throughput Experiments 50%
• Imbalanced Data 50%
• Knowledge Integration 50%
• Machine Learning 50%
• Machine Learning Approach 100%
• Manual Inspection 50%
• Multi-class 100%
• Multiple Data Sources 50%
• Multiple Data Types 50%
• Protein Fold Classification 100%
• Protein Folding 50%
• Protein Structural Class 50%
• Protein Structure 50%
• Structural Genomics 50%
• Structural Information 50%
• Traditional Techniques 50%
• Unbalanced Sample 50%

Computer Science

• Base Classifier 50%
• Data Source 100%
• Experimental Result 50%
• High Throughput 50%
• Imbalanced Data 50%
• Learning System 100%
• Machine Learning 100%
• Machine Learning Approach 100%
• Multiple Data Source 50%
• Multiple Data Type 50%
• Traditional Method 50%

Biochemistry, Genetics and Molecular Biology

• Biological Database 50%
• Evolution 50%
• Protein Fold Class 100%
• Protein Structure 100%
• Protein Tertiary Structure 100%
• Structural Genomics 50%