A multiple kernel support vector machine scheme for feature selection and rule extraction from gene expression data of cancer tissue

The application of gene expression data to the diagnosis and classification of cancer has become a hot issue in the field of cancer classification. Gene expression data usually contains a large number of tumor-free data and has the characteristics of high dimensions. In order to select determinant genes related to breast cancer from the initial gene expression data, we propose a new feature selection method, namely, support vector machine based on recursive feature elimination and parameter optimization (SVM-RFE-PO). The grid search (GS) algorithm, the particle swarm optimization (PSO) algorithm, and the genetic algorithm (GA) are applied to search the optimal parameters in the feature selection process. Herein, the new feature selection method contains three kinds of algorithms: support vector machine based on recursive feature elimination and grid search (SVM-RFE-GS), support vector machine based on recursive feature elimination and particle swarm optimization (SVM-RFE-PSO), and support vector machine based on recursive feature elimination and genetic algorithm (SVM-RFE-GA). Then the selected optimal feature subsets are used to train the SVM classifier for cancer classification. We also use random forest feature selection (RFFS), random forest feature selection and grid search (RFFS-GS), and minimal redundancy maximal relevance (mRMR) algorithm as feature selection methods to compare the effects of the SVM-RFE-PO algorithm. The results showed that the feature subset obtained by feature selection using SVM-RFE-PSO algorithm results has a better prediction performance of Area Under Curve (AUC) in the testing data set. This algorithm not only is time-saving, but also is capable of extracting more representative and useful genes.

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Random-Forest (RF) and Support Vector Machine (SVM) Implementation for Analysis of Gene Expression Data in Chronic Kidney Disease (CKD)

IOP Conference Series Materials Science and Engineering ◽

10.1088/1757-899x/546/5/052066 ◽

2019 ◽

Vol 546 ◽

pp. 052066 ◽

Cited By ~ 1

Author(s):

Zuherman Rustam ◽

Ely Sudarsono ◽

Devvi Sarwinda

Keyword(s):

Gene Expression ◽

Chronic Kidney Disease ◽

Support Vector Machine ◽

Random Forest ◽

Kidney Disease ◽

Gene Expression Data ◽

Support Vector ◽

Expression Data

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A COMPARATIVE STUDY ON GENE SELECTION METHODS FOR TISSUES CLASSIFICATION ON LARGE SCALE GENE EXPRESSION DATA

Jurnal Teknologi ◽

10.11113/jt.v78.8843 ◽

2016 ◽

Vol 78 (5-10) ◽

Author(s):

Farzana Kabir Ahmad

Keyword(s):

Gene Expression ◽

Feature Selection ◽

Gene Expression Data ◽

Large Scale ◽

Gene Selection ◽

Support Vector ◽

Breast Cancer Dataset ◽

Expression Data ◽

Selection Methods ◽

Normal Tissues

Deoxyribonucleic acid (DNA) microarray technology is the recent invention that provided colossal opportunities to measure a large scale of gene expressions simultaneously. However, interpreting large scale of gene expression data remain a challenging issue due to their innate nature of “high dimensional low sample size”. Microarray data mainly involved thousands of genes, n in a very small size sample, p which complicates the data analysis process. For such a reason, feature selection methods also known as gene selection methods have become apparently need to select significant genes that present the maximum discriminative power between cancerous and normal tissues. Feature selection methods can be structured into three basic factions; a) filter methods; b) wrapper methods and c) embedded methods. Among these methods, filter gene selection methods provide easy way to calculate the informative genes and can simplify reduce the large scale microarray datasets. Although filter based gene selection techniques have been commonly used in analyzing microarray dataset, these techniques have been tested separately in different studies. Therefore, this study aims to investigate and compare the effectiveness of these four popular filter gene selection methods namely Signal-to-Noise ratio (SNR), Fisher Criterion (FC), Information Gain (IG) and t-Test in selecting informative genes that can distinguish cancer and normal tissues. In this experiment, common classifiers, Support Vector Machine (SVM) is used to train the selected genes. These gene selection methods are tested on three large scales of gene expression datasets, namely breast cancer dataset, colon dataset, and lung dataset. This study has discovered that IG and SNR are more suitable to be used with SVM. Furthermore, this study has shown SVM performance remained moderately unaffected unless a very small size of genes was selected.

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