Given multiple possible models for a protein structure, a common sub-task in in-silico Protein Structure Prediction is ranking these models according to their quality. <p> Extant approaches use MLE estimates of parameters to obtain point estimates of the Model Quality. We describe a Bayesian alternative to assessing the quality of these models that builds an MRF over the parameters of each model and performs approximate inference to integrate over them. Hyper-parameters are learnt by optimizing a list-wise loss function over training data. <p> Our results indicate that our Bayesian approach can significantly outperform MLE estimates and that optimizing the hyper-parameters can further improve results.
Discussion
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