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 ### Step 2 : Do differential expression analysis for feature selection
 Run the file "liver cancer mirna_mrna and methylation differential analysis.R" using R or R Studio. It will use the files from "liver" folder as input. Differentially expressed columns will be found for each type of data(mrna, mirna, methylation). Files named "Diff_mrnaupdated.csv", "Diff_methylupdated.csv" and "Diff_mirnaupdated.csv" will be generated and will be used in Step 4.
 
+(Some of the output files generated for the Differential expression data produced could be commented out, to generate the output for those data please uncomment those lines).
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 ### Step 3 : Do logistic regression analysis for feature selection + Machie Learling classification models for it
 Run the python notebook "Logreg_part.ipynb" using Jupyter Notebook or Colab. It will use the files from "liver" folder as input. Number of features will be reduced using logistic regression. 2 binary classification models will be trained. "imp_features_svm_logreg.csv" and "imp_features_gb_logreg.csv" will be generated as output files, but only the first one will be used in Step 5 as results of the best model. The latter is provided if wanted for additional evaluation