An ensemble deep learning model for predicting minimum inhibitory concentrations of antimicrobial peptides against pathogenic bacteria

Keyphrases

• Minimum Inhibitory Concentration 100%
• Pathogenic Bacteria 100%
• Antimicrobial Peptides 100%
• Ensemble Deep Learning Model 100%
• Antibiotic Resistance 40%
• Ensemble Model 40%
• Inhibitory Effect 20%
• Model Combining 20%
• Model Performance 20%
• Regression Model 20%
• Bacterial Strains 20%
• Convolutional Neural Network 20%
• Deep Learning 20%
• Superior Performance 20%
• Peptide Sequencing 20%
• Effective Alternative 20%
• AI-based Methods 20%
• Pearson Correlation Coefficient 20%
• Concentration Value 20%
• Pseudomonas Aeruginosa (P. aeruginosa) 20%
• Bidirectional Long Short-term Memory (BiLSTM) 20%
• Sequence Features 20%
• Alternative Therapy 20%
• Priority Pathogens 20%
• Protein Language Model 20%
• Peptide Application 20%
• Multi-branch Model 20%
• Genomic Features 20%
• Model Embedding 20%
• Staphylococcus Aureus ATCC 25923 20%
• Escherichia Coli ATCC 25922 20%

Biochemistry, Genetics and Molecular Biology

• Antimicrobial Peptides 100%
• Minimum Inhibitory Concentration 100%
• Antibiotic Resistance 40%
• Escherichia coli 20%
• Bacterial Strain 20%
• Peptide Sequence 20%
• Infectious Agent 20%
• Short Term Memory 20%
• Staphylococcus Aureus 20%
• Artificial Intelligence 20%
• Genomics 20%
• Pseudomonas aeruginosa 20%