An ensemble learning approach to reverse-engineering transcriptional regulatory networks from time-series gene expression data

Keyphrases

• Transcription Factor 100%
• Transcriptional Network 100%
• Time-course Gene Expression 100%
• Reverse Engineering 100%
• Ensemble Learning Approach 100%
• Yeast Cell Cycle 33%
• Decision Tree Ensemble 33%
• Gene Expression 16%
• Biological Processes 16%
• Microarray Data 16%
• Gene Transcription 16%
• Gene Promoter 16%
• Microarray 16%
• Expression Level 16%
• Binding Data 16%
• Transcription Factor Binding 16%
• Biological Events 16%
• Chromatin Immunoprecipitation (ChIP) 16%
• Large Amount of Data 16%
• Challenging Tasks 16%
• Budding Yeast 16%
• Cell Cycle Phases 16%
• Logic Rules 16%
• Postgenomics 16%
• Supervised Machine Learning 16%
• Decision Tree Algorithm 16%
• Special Regulations 16%
• Expression Data 16%
• Yeast Gene 16%
• Validation Experiment 16%
• Synergetics 16%
• ChIP-chip 16%
• Main Idea 16%
• Gene Transcriptional Regulation 16%
• Ensemble Learning Method 16%
• Series Expression 16%
• Spline Interpolation Method 16%

Biochemistry, Genetics and Molecular Biology

• Transcription Factors 100%
• Gene Expression Data 100%
• Regulatory Network 100%
• Cell Cycle 42%
• Decision Trees 42%
• Microarrays 28%
• Gene Expression 14%
• Biological Phenomena and Functions Concerning the Entire Organism 14%
• Genetic Transcription 14%
• Chromatin Immunoprecipitation 14%
• Pribnow Box 14%
• Budding Yeast 14%
• Cell Cycle Phase 14%
• Supervised Machine Learning 14%
• Genomics 14%