Neural networks enable efficient and accurate simulation-based inference of evolutionary parameters from adaptation dynamics

Keyphrases

• Neural Network 100%
• Copy number Variation 100%
• Efficient Simulation 100%
• Evolutionary Parameter 100%
• Adaptation Dynamics 100%
• Accurate Simulation 100%
• Simulation-based Inference 100%
• Fitness Effects 30%
• Adaptive Evolution 20%
• Evolutionary Model 20%
• GAP1 20%
• Inference Methods 20%
• Approximate Bayesian Computation 20%
• Beneficial mutations 20%
• Chemostat 20%
• Rapid Adaptive Evolution 20%
• Neural Posteriorization 20%
• Saccharomyces Cerevisiae 10%
• Supply Rate 10%
• Estimated Rates 10%
• Cell Division 10%
• Tumor 10%
• Glutamine 10%
• Simulation-based 10%
• Single nucleotide Variant 10%
• Evolutionary Process 10%
• Artificial Neural Network 10%
• Heritable Variation 10%
• Evolutionary Change 10%
• Estimation Algorithms 10%
• Posterior Distribution 10%
• Inference Approach 10%
• De-novo mutations 10%
• Formation Rate 10%
• Locus-specific 10%
• Inference Algorithms 10%
• Inference Framework 10%
• Viral Evolution 10%
• Lineage Tracing 10%
• Inference Model 10%
• Evolving Populations 10%
• Nutrient Limitation 10%
• Expected Fitness 10%
• Likelihood-free Inference 10%
• Sequential Monte Carlo Algorithm 10%
• Sequential Monte Carlo 10%
• Chemostat Model 10%

Immunology and Microbiology

• Dynamics 100%
• Evolutionary Adaptation 100%
• Chemostat 75%
• Saccharomyces cerevisiae 25%
• Cell Division 25%
• Lineages 25%
• Virus Evolution 25%
• Artificial Neural Network 25%

Biochemistry, Genetics and Molecular Biology

• Dynamics 100%
• Evolutionary Adaptation 100%
• Saccharomyces cerevisiae 25%
• Glutamine 25%
• Cell Division 25%
• Artificial Neural Network 25%