Sep 19, 2008 · Our results confirm that highly unbalanced training sets can reduce the accuracy of classifier predictions and show that, in the peptide-MHCI ...
Sep 19, 2008 · Conclusion: Our method consistently improves the performance of decision trees in predicting peptide-MHC class I binding by using cost-balancing ...
In this paper, we purposed a novel method for peptide-MHC-I binding prediction. Since deep learning is developing fast, we consider that it has more advantages ...
Oct 18, 2021 · In this work, we propose a new deep learning model, MHCrank , to predict the probability that a peptide will be processed for presentation ...
Our results suggest that prediction methods for HLA-binding peptides should be updated as HLA-peptide-binding knowledge increases. Introduction. Major ...
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We present NetMHCpan-4.0, a method trained on both binding affinity and eluted ligand data leveraging the information from both data types.
Specifically, we consider the application of deep learning models pretrained on large datasets of protein sequences to predict MHC Class I-peptide binding.
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This study delves into the limitations of current methods and benchmarks for MHC-I presentation. We introduce a novel benchmark designed to assess ...
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Without oversampling model was overfitting to the dominant class, because of the data imbalance, we oversampled the training dataset with different frequencies ...
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Jun 13, 2017 · This imbalance made us suggest a novel machine learning approach integrating information from both types of data (binding affinity and MS ...