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View Code? Open in Web Editor NEWa brand-new deep learning-based prediction model for glutarylation sites named DeepDN-iGlu adopting attention residual learning method and DenseNet.
a brand-new deep learning-based prediction model for glutarylation sites named DeepDN-iGlu adopting attention residual learning method and DenseNet.
To facilitate the access to glutarylation site prediction data, DeepDN_iGlu has been implemented as an available web server ( https://bioinfo.wugenqiang.top/~smw/DeepDN_iGlu/ ). The directory structure of this repository is as follows: Images used in the paper: All images used in the paper gitproject: gitproject is the project code used for this research ----subdir:dataset: benchmark data set ----subdir:feture_method: including feature encoding method and feature files that has been generated ----subdir:model: the final model generated in the experiment ----subdir:result: the result information ----main file: data_processing.py: process the raw data predicting: get start to predict supplements: The 10-fold cross-validation results for the final model and comparison models are included in supplements ----subdir:10-fold cross-validation results of final model: --------subdir:10-fold cross-validation: Contains the model generated by ten-folds cross-validation, related pictures(loss and MCC of training and validation sets) and log of history(tf log) --------file:statistics_eval_param.csv: the performance of 10-fold cross-validation ----subdir:models of comparison: --------subdir:Cross entropy loss:Ten-fold cross-validation results when using the cross-entropy loss function --------subdir:dense blocks number selection:Ten-fold cross-validation results when choosing different numbers of dense blocks
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