ArticleCell journal2023
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.
Article in Cell journal, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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2 citing papers in PubMed, 2 citations in OpenAlex.
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- Structure-activity relationships for the G-quadruplex-targeting experimental drug QN-302 and two analogues probed with comparative transcriptome profiling and molecular modeling.Scientific reports · 2024Article
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4 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveIn 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 as missing. It is a difficult task to determine the genes that cause disease or cancer from a large number of genes. This study aimed to find effective genes in pancreatic cancer (PC). First, the K-nearest neighbor (KNN) imputation method was used to solve the problem of missing values (MVs) of gene expression. Then, the random forest algorithm was used to identify the genes associated with PC. MATERIALS AND
methodsIn this retrospective study, 24 samples from the GSE14245 dataset were examined. Twelve samples were from patients with PC, and 12 samples were from healthy control. After preprocessing and applying the fold-change technique, 29482 genes were used. We used the KNN imputation method to impute when a particular gene had MVs. Then, the genes most strongly associated with PC were selected using the random forest algorithm. We classified the dataset using support vector machine (SVM) and naïve bayes (NB) classifiers, and F-score and Jaccard indices were reported.
resultsOut of the 29482 genes, 1185 genes with fold-changes greater than 3 were selected. After selecting the most associated genes, 21 genes with the most important value were identified.
conclusionThis study is based on the application of the fold change technique, imputation method, and random forest algorithm and could find the most associated genes that were not identified in many studies. We therefore suggest researchers use the random forest algorithm to detect the related genes within the disease of interest.
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