pred_ensembel {HPiP} | R Documentation

## Predict Interactions via Ensemble Learning Method

### Description

This function uses an ensemble of classifiers to predict interactions from the sequence-based dataset. This ensemble algorithm combines different results generated from individual classifiers within the ensemble via average to enhance prediction.

### Usage

    pred_ensembel(
      features,
      gold_standard,
      classifier = c("avNNet", "svmRadial", "ranger"),
      resampling.method = "cv",
      ncross = 2,
      repeats = 2,
      verboseIter = TRUE,
      plots = TRUE,
      filename = "plots.pdf"
    )

### Arguments

`features` | A data frame with host-pathogen protein-protein interactions (HP-PPIs) in the first column, and features to be passed to the classifier in the remaining columns.
`gold_standard` | A data frame with gold_standard HP-PPIs and class label indicating if such PPIs are positive or negative.
`classifier` | The type of classifier to use. See `caret` for the availbale classifiers.
`resampling.method` | The resampling method:'boot', 'boot632', 'optimism_boot', boot_all', 'cv', 'repeatedcv', 'LOOCV', 'LGOCV'; defaults to cv. See `trainControl` for more details.
`ncross` | Number of partitions for cross-validation; defaults to 5.See `trainControl` for more details.
`repeats` | for repeated k-fold cross validation only; defaults to 3.See `rfeControl` for more details.
`verboseIter` | Logical value, indicating whether to check the status of training process;defaults to FALSE.
`plots` | Logical value, indicating whether to plot the performance of ensemble learning algorithm as compared to individual classifiers; defaults to TRUE.If the argument set to TRUE, plots will be saved in the current working directory. These plots are : pr_plot - Precision-recall plot of ensemble classifier vs selected individual classifiers. roc_plot - ROC plot of ensemble classifier vs selected individual classifiers. points_plot - Plot accuracy, F1-score ,positive predictive value (PPV),sensitivity (SE), and Matthews correlation coefficient (MCC) of ensemble classifier vs selected individual classifiers.
`filename` | A character string, indicating the output filename as an pdf object.

### Details

pred_ensembel

### Value

Ensemble_training_output

- prediction score - Prediction scores for whole dataset from each individual classifier.

- Best - Selected hyper parameters.

- Parameter range - Tested hyper parameters.

- prediction_score_test - Scores probabilities for test data from each individual classifier.

- class_label - Class probabilities for test data from each individual classifier.

classifier_performance

- cm - A confusion matrix.

- ACC - Accuracy.

- SE - Sensitivity.

- SP - Specificity.

- PPV - Positive Predictive Value.

- F1 - F1-score.

- MCC - Matthews correlation coefficient.

- Roc_Object - A list of elements. See `roc` for more details.

- PR_Object - A list of elements. See `pr.curve` for more details.

predicted_interactions - The input data frame of pairwise interactions, including classifier scores averaged across all models.

### Author(s)

Matineh Rahmatbakhsh, matinerb.94@gmail.com

### Examples

    data('example_data')
    features <- example_data[, -2]
    gd <- example_data[, c(1,2)]
    gd <- na.omit(gd)
    ppi <-pred_ensembel(features,gd,
    classifier = c("avNNet", "svmRadial", "ranger"),
    resampling.method = "cv",ncross = 2,verboseIter = FALSE,plots = FALSE,
    filename = "plots.pdf")
    #extract predicted interactions
    pred_interaction <- ppi[["predicted_interactions"]]

[Package HPiP version 1.0.0 Index]