A Deep Learning Model to Predict the ncRNA-Protein Interactions Based on Sequences Information Only

Keyphrases

• Non-coding RNA (ncRNA) 100%
• Deep Learning Model 100%
• XGBoost 100%
• Sequence Information 100%
• Stacked Denoising Autoencoder 100%
• Interaction-based 100%
• Benchmark Dataset 50%
• Computational Approach 25%
• RNA-protein Complex 25%
• RNA Sequencing (RNA-seq) 25%
• Deep Learning 25%
• Dysregulation 25%
• Computational Methods 25%
• Biological Processes 25%
• Severe Disease 25%
• RNA Interaction 25%
• Protein Synthesis 25%
• Hidden Characteristics 25%
• Protein-protein Interaction Prediction 25%
• RNA Processing 25%
• Meta-learner 25%
• Generalization Power 25%
• Biological Research 25%
• Vector Features 25%
• 3-mers 25%
• Conjoint Triad Feature 25%
• MiRNA-mRNA Interaction 25%

Computer Science

• Deep Learning Model 100%
• Extreme Gradient Boosting 100%
• Information Sequence 100%
• Denoising Autoencoder 100%
• Learning Approach 25%
• Computational Approach 25%
• Computational Method 25%
• Baseline Model 25%
• Dimensional Vector 25%
• Deep Learning Method 25%
• Amino Acid Sequence 25%
• Separate Network 25%

Biochemistry, Genetics and Molecular Biology

• Non-Coding RNA 100%
• Protein Interaction 100%
• Xgboost 50%
• RNA Sequence 12%
• Gene Expression 12%
• Biological Phenomena and Functions Concerning the Entire Organism 12%
• Peptide Sequence 12%
• Protein Biosynthesis 12%
• RNA Processing 12%