The Performance Evaluation of The Random Forest Algorithm for A Gene Selection in Identifying Genes Associated with Resectable Pancreatic Cancer in Microarray Dataset: A Retrospective Study

Abstract

Objective: In microarray datasets, hundreds and thousands of genes are measured in a small number of samples,and sometimes due to problems that occur during the experiment, the expression value of some genes is recorded asmissing. It is a difficult task to determine the genes that cause disease or cancer from a large number of genes. Thisstudy aimed to find effective genes in pancreatic cancer (PC). First, the K-nearest neighbor (KNN) imputation methodwas used to solve the problem of missing values (MVs) of gene expression. Then, the random forest algorithm wasused to identify the genes associated with PC.Materials and Methods: In this retrospective study, 24 samples from the GSE14245 dataset were examined. Twelvesamples were from patients with PC, and 12 samples were from healthy control. After preprocessing and applying thefold-change technique, 29482 genes were used. We used the KNN imputation method to impute when a particulargene had MVs. Then, the genes most strongly associated with PC were selected using the random forest algorithm. Weclassified the dataset using support vector machine (SVM) and naïve bayes (NB) classifiers, and F-score and Jaccardindices were reported.Results: Out of the 29482 genes, 1185 genes with fold-changes greater than 3 were selected. After selecting the mostassociated genes, 21 genes with the most important value were identified. S100P and GPX3 had the highest andlowest importance values, respectively. The F-score and Jaccard value of the SVM and NB classifiers were 95.5, 93,92, and 92 percent, respectively.Conclusion: This study is based on the application of the fold change technique, imputation method, and randomforest algorithm and could find the most associated genes that were not identified in many studies. We thereforesuggest researchers use the random forest algorithm to detect the related genes within the disease of interest.

Keywords

• classification
• microarray analysis
• neoplasms
• pancreas