A Bayesian data fusion based approach for learning genome-wide transcriptional regulatory networks

Mathematics

• Data Fusion 100%
• Regulatory Networks 96%
• Transcription Factor 76%
• Genomics 75%
• Genome 71%
• Learning 58%
• Prior Knowledge 38%
• Hybrid Learning 33%
• Framework 27%
• Robustness 26%
• Crosstalk 26%
• Data Integration 25%
• Structure Learning 25%
• Reverse Engineering 24%
• Network Algorithms 23%
• High Throughput 22%
• Snapshot 22%
• Systems Biology 21%
• Chip 20%
• Gene Expression 20%
• Reconstruction Algorithm 20%
• Sequencing 19%
• Learning Algorithm 19%
• High-dimensional Data 18%
• Data-driven 18%
• False 16%
• Gene 15%
• Benchmark 15%
• Background 14%
• Target 13%
• Methodology 11%
• Interaction 11%
• Modeling 10%
• Model 5%

Engineering & Materials Science

• Transcription factors 92%
• Data fusion 68%
• Genomics 67%
• Systems Biology 28%
• Reverse engineering 24%
• Data integration 24%
• Crosstalk 24%
• Gene expression 23%
• DNA 22%
• Genes 20%
• Learning algorithms 17%
• Throughput 14%

Medicine & Life Sciences

• Gene Regulatory Networks 70%
• Learning 47%
• Genome 44%
• Transcription Factors 36%
• Chromatin Immunoprecipitation Sequencing 20%
• Systems Biology 19%
• Benchmarking 17%
• Genomics 16%
• Noise 14%
• Datasets 12%
• Aptitude 11%
• Gene Expression 9%
• DNA 9%
• Genes 6%