svmTrain: svmTrain

In Babulab-bioc/MSiP: 'MassSpectrometry' Interaction Prediction

Description

The labeled feature matrix can be used as input for Support Vector Machines (SVM) classifier. The classifier then assigns each bait-prey pair a confidence score, indicating the level of support for that pair of proteins to interact. Hyperparameter optimization can also be performed to select a set of parameters that maximizes the model's performance. This function also computes the areas under the precision-recall (PR) and ROC curve to evaluate the performance of the classifier.

Usage

svmTrain(
  dtInput,
  impute = TRUE,
  p = 0.3,
  parameterTuning = TRUE,
  cost = seq(from = 2, to = 10, by = 2),
  gamma = seq(from = 0.01, to = 0.1, by = 0.02),
  kernel = "radial",
  ncross = 10,
  pr.plot = TRUE,
  roc.plot = TRUE
)

Arguments

dtInput	Data frame containing instances with class labels
impute	Logical value, indicating whether to impute missing values
p	The percentage of data that goes to training; defaults to 0.3
parameterTuning	Logical value; indicating whether to tune SVM hyper parameters
cost	Cost of constraints violation
gamma	Parameter needed for all kernels except linear
kernel	Kernel type: 'linear', 'polynomial', 'sigmoid', or 'radial'; defaults to 'radial'
ncross	K-fold cross validation on the training data is performed to assess the quality of the model; defaults to 10
pr.plot	Logical value, indicating whether to plot precision-recall (PR) curve
roc.plot	Logical value, indicating whether to plot ROC curve

Value

Data frame containing a classification results for all instances in the data set, where positive confidence score corresponds to the level of support for the pair of proteins to be true positive, whereas negative score corresponds to the level of support for the pair of proteins to be true negative.

Author(s)

Matineh Rahmatbakhsh, [e-mail]

Examples

data(testdfClassifier)
predidcted_SVM <-
svmTrain(testdfClassifier,impute = FALSE,p = 0.3,parameterTuning = FALSE,
cost = seq(from = 2, to = 10, by = 2),
gamma = seq(from = 0.01, to = 0.10, by = 0.02),
kernel = "radial",ncross = 10,
pr.plot = FALSE, roc.plot = TRUE)
head(predidcted_SVM)

Babulab-bioc/MSiP documentation built on Dec. 17, 2021, 9:52 a.m.