DeepCRISTL: deep transfer learning to predict CRISPR/Cas9 on-target editing efficiency in specific cellular contexts

Keyphrases

• Cas9 Protein 100%
• Cellular Context 100%
• Deep Transfer Learning 100%
• Editing Efficiency 100%
• High-throughput 55%
• Guide RNA (gRNA) 44%
• Transfer Learning 22%
• Machine Learning Models 22%
• Computational Methods 11%
• RNA Editing 11%
• State-of-the-art Techniques 11%
• Biologically Relevant 11%
• Deep Learning 11%
• Performance Prediction 11%
• Best Performers 11%
• Saliency Map 11%
• Low Correlation 11%
• Genomic Loci 11%
• CRISPR-Cas9 System 11%
• Gene Editing 11%
• CRISPR-Cas9 Editing 11%
• Efficiency Prediction 11%
• State-of-the-art Models 11%
• Model Weights 11%

Computer Science

• High Throughput 100%
• Deep Transfer Learning 100%
• Transfer Learning 40%
• Machine Learning Model 40%
• Computational Method 20%
• Learned Feature 20%
• Deep Learning Model 20%
• Learning Approach 20%
• General Pattern 20%
• Low Correlation 20%
• Prediction Performance 20%

Biochemistry, Genetics and Molecular Biology

• CRISPR/Cas9 100%
• Transfer of Learning 100%
• Guide RNA 100%
• RNA Editing 25%
• Cas9 25%
• Genomics 25%
• Cas9 Protein 25%