MAPC-GibbsOS: An integrated approach for robust identification of gene regulatory networks

Keyphrases

• Gene Regulatory Network 100%
• Robust Identification 100%
• Gene Expression Data 66%
• Binding Data 50%
• Transcription Factor 33%
• Clustering Methods 33%
• Breast Cancer Recurrence 33%
• Modularized 33%
• Noise in Gene Expression 33%
• Modularized Structure 33%
• Affinity Propagation Clustering 33%
• Cancer Research 16%
• In Cancer 16%
• Simulation Study 16%
• Biological Studies 16%
• Breast Cancer Patients 16%
• Performance Improvement 16%
• Network Identification 16%
• Sampling Methods 16%
• Transcriptional Network 16%
• Noise Effects 16%
• Regulation Mechanism 16%
• Functional Annotation 16%
• Gene Cluster 16%
• Signal-to-noise Ratio 16%
• False Positive Rate 16%
• Accuracy Improvement 16%
• Simulation Data 16%
• Microarray Data 16%
• Cancer Gene Expression 16%
• Integration Method 16%
• Background Estimation 16%
• Two-step Approach 16%
• Robustness to Noise 16%
• Gibbs Sampler 16%
• Large Noise 16%
• Binding Motif 16%
• Similarity Measure 16%
• Breast Cancer Genes 16%
• Gene Module 16%
• Co-regulated Genes 16%
• Regulatory Structure 16%
• Factor Identification 16%
• Data Motif 16%
• Clustering Factor 16%
• Novel Clustering 16%
• Target Gene Identification 16%

Biochemistry, Genetics and Molecular Biology

• Gene Regulatory Network 100%
• Gene Expression Data 60%
• Regulatory Network 40%
• Transcription Factors 40%
• Gene Expression 20%
• Signal Noise Ratio 20%
• Microarrays 20%
• Gene Cluster 20%