runNaiveBayesClassifier: Run the OncoSig Naive Bayes Classifier as used in the...
In califano-lab/OncoSig: What the package does (short line)

1	runNaiveBayesClassifier()

runNaiveBayesClassifier()
## The function is currently defined as
function () 
{
    df_1 = read.delim("Input_data_files/Naive_Bayes_evidences_set_1.txt", 
        header = TRUE)
    df_2 = read.delim("Input_data_files/Naive_Bayes_evidences_set_2.txt", 
        header = TRUE)
    the_bins = list(c(0, 40, 200, 1200), c(0, 0.1), c(-2, -0.15, 
        -0.02, 0.0925), c(1, 2, 6), c(0, 0.25), c(1, 3, 20), 
        c(1, 4, 20), c(1, 4, 20), c(0, 1e-04, 0.9999), c(0, 0.01, 
            0.05))
    correlated_features = grep("MS_", colnames(df_1), value = TRUE)
    message("Calculating LR_posterior for fold two holdout set\n")
    the_results_set_1 = OncoSigNB(df_1, df_2, the_bins, correlated_features)
    message("Calculating LR_posterior for fold one holdout set\n")
    the_results_set_2 = OncoSigNB(df_2, df_1, the_bins, correlated_features)
    the_results_set_2_rank = cbind(the_results_set_2, rank(-the_results_set_2))
    the_results_set_1_rank = cbind(the_results_set_1, rank(-the_results_set_1))
    temp = rbind(the_results_set_1_rank, the_results_set_2_rank)
    temp = as.data.frame(temp)
    colnames(temp) = c("LR_post", "Rank")
    cross_validated_predictions = temp[order(temp$Rank), ]
    return(cross_validated_predictions)
  }