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Individual models have their individual faults, of course.
Individual models have different limitations.
Individual models have specific targets based on their footprint.
First, note that, in the current DL-CHI formulation, the individual models have to be learned via the CHI formulation without information from the dictionary.
Most machine learning methods take only peptide sequences as input features; therefore, individual models have to be developed for each HLA.
On a sample basis, individual models have been selected and manually compared to the pre-computed plots provided by BioModels Database in order to check the correctness of the simulation results.
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Individual models had modest performance on the test data set (AUC 0.71, 0.79 and 0.79 respectively).
The diagnostic accuracies in the test set, after the four individual models had been integrated into a single model, improved to 64.0% and 55.2% in the male and female patient groups, respectively.
The diagnostic accuracies in the training set, after the four individual models had been integrated into a single model, improved to 69.3% and 63.5% in the male and female patient groups, respectively; however, when the model was applied to the test set, the accuracies decreased to 61.4% in the male patients and 52.2% in the female patients.
Each individual model has a tree structure, where each node of the tree represents a binary partition.
The single-individual model has generally been used in human studies, with the exception of one work in which the mixed-effects model was applied to a group of elite swimmers [ 7].
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CEO of Professional Science Editing for Scientists @ prosciediting.com