Man pages for MantaID
A Machine-Learning Based Tool to Automate the Identification of Biological Database IDs
| Example | ID example dataset. |
| mi | A wrapper function that executes MantaID workflow. |
| mi_balance_data | Data balance. Most classes adopt random undersampling, while... |
| mi_clean_data | Reshape data and delete meaningless rows. |
| mi_data_attributes | ID-related datasets in biomart. |
| mi_data_procID | Processed ID data. |
| mi_data_rawID | ID dataset for testing. |
| mi_filter_feat | Performing feature selection in a automatic way based on... |
| mi_get_confusion | Compute the confusion matrix for the predicted result. |
| mi_get_ID | Get ID data from the 'Biomart' database using 'attributes'. |
| mi_get_ID_attr | Get ID attributes from the 'Biomart' database. |
| mi_get_importance | Plot the bar plot for feature importance. |
| mi_get_miss | Observe the distribution of the false response of the test... |
| mi_get_padlen | Get max length of ID data. |
| mi_plot_cor | Plot correlation heatmap. |
| mi_plot_heatmap | Plot heatmap for result confusion matrix. |
| mi_predict_new | Predict new data with a trained learner. |
| mi_run_bmr | Compare classification models with small samples. |
| mi_split_col | Cut the string of ID column character by character and divide... |
| mi_split_str | Split the string into individual characters and complete the... |
| mi_to_numer | Convert data to numeric, and for the ID column convert with... |
| mi_train_BP | Train a three layers neural network model. |
| mi_train_rg | Random Forest Model Training. |
| mi_train_rp | Classification tree model training. |
| mi_train_xgb | Xgboost model training |
| mi_tune_rg | Tune the Random Forest model by hyperband. |
| mi_tune_rp | Tune the Decision Tree model by hyperband. |
| mi_tune_xgb | Tune the Xgboost model by hyperband. |
| mi_unify_mod | Predict with four models and unify results by the sub-model's... |
