Random forest for gene selection and microarray data classification
نویسندگان
چکیده
A random forest method has been selected to perform both gene selection and classification of the microarray data. In this embedded method, the selection of smallest possible sets of genes with lowest error rates is the key factor in achieving highest classification accuracy. Hence, improved gene selection method using random forest has been proposed to obtain the smallest subset of genes as well as biggest subset of genes prior to classification. The option for biggest subset selection is done to assist researchers who intend to use the informative genes for further research. Enhanced random forest gene selection has performed better in terms of selecting the smallest subset as well as biggest subset of informative genes with lowest out of bag error rates through gene selection. Furthermore, the classification performed on the selected subset of genes using random forest has lead to lower prediction error rates compared to existing method and other similar available methods.
منابع مشابه
Classification and Biomarker Genes Selection for Cancer Gene Expression Data Using Random Forest
Background & objective: Microarray and next generation sequencing (NGS) data are the important sources to find helpful molecular patterns. Also, the great number of gene expression data increases the challenge of how to identify the biomarkers associated with cancer. The random forest (RF) is used to effectively analyze the problems of large-p and smal...
متن کاملSFLA Based Gene Selection Approach for Improving Cancer Classification Accuracy
In this paper, we propose a new gene selection algorithm based on Shuffled Frog Leaping Algorithm that is called SFLA-FS. The proposed algorithm is used for improving cancer classification accuracy. Most of the biological datasets such as cancer datasets have a large number of genes and few samples. However, most of these genes are not usable in some tasks for example in cancer classification....
متن کاملFeature Selection and Classification of Microarray Gene Expression Data of Ovarian Carcinoma Patients using Weighted Voting Support Vector Machine
We can reach by DNA microarray gene expression to such wealth of information with thousands of variables (genes). Analysis of this information can show genetic reasons of disease and tumor differences. In this study we try to reduce high-dimensional data by statistical method to select valuable genes with high impact as biomarkers and then classify ovarian tumor based on gene expression data of...
متن کاملPrediction of blood cancer using leukemia gene expression data and sparsity-based gene selection methods
Background: DNA microarray is a useful technology that simultaneously assesses the expression of thousands of genes. It can be utilized for the detection of cancer types and cancer biomarkers. This study aimed to predict blood cancer using leukemia gene expression data and a robust ℓ2,p-norm sparsity-based gene selection method. Materials and Methods: In this descriptive study, the microarray ...
متن کاملInvestigation of Random Forest Performance with Cancer Microarray Data
The diagnosis of cancer type based on microarray data offers hope that cancer classification can be highly accurate for clinicians to choose the most appropriate forms of treatment with it. Due to several inherent characteristics associated with microarray data, accurate diagnosis has been an active research topic attracting tremendous research interests in machine learning community. In this p...
متن کاملGene Selection Using Random Forest and Proximity Differences Criterion on DNA Microarray Data
Selection of relevant genes for sample classification is a common task in most gene expression studies. As a powerful classification approach, random forest has been applied in this field, and it shows excellent performance compared with other classification methods. The measure of variable importance is the key of gene selection using random forest. However, the existing methods just consider ...
متن کامل