An Efficient Gene Selection Technique based on Fuzzy C-means and Neighborhood Rough Set

2014 ◽  
Vol 8 (6) ◽  
pp. 3101-3110 ◽  
Author(s):  
Jiucheng Xu ◽  
Tianhe Xu ◽  
Lin Sun ◽  
Jinyu Ren
Keyword(s):  
Rough Set ◽  
Gene Selection ◽  
Selection Technique ◽  
Fuzzy C Means ◽  
Efficient Gene ◽  
Neighborhood Rough Set

Gene Selection Method for Microarray Data Classification Using Particle Swarm Optimization and Neighborhood Rough Set

2019 ◽  
Vol 14 (5) ◽  
pp. 422-431 ◽  
Author(s):  
Mingquan Ye ◽  
Weiwei Wang ◽  
Chuanwen Yao ◽  
Rong Fan ◽  
Peipei Wang
Keyword(s):  
Particle Swarm Optimization ◽  
Rough Set ◽  
Microarray Data ◽  
Gene Selection ◽  
Particle Swarm ◽  
Research Trend ◽  
Swarm Optimization ◽  
Knn Classifier ◽  
Tumor Identification ◽  
Neighborhood Rough Set

Background: Mining knowledge from microarray data is one of the popular research topics in biomedical informatics. Gene selection is a significant research trend in biomedical data mining, since the accuracy of tumor identification heavily relies on the genes biologically relevant to the identified problems. Objective: In order to select a small subset of informative genes from numerous genes for tumor identification, various computational intelligence methods were presented. However, due to the high data dimensions, small sample size, and the inherent noise available, many computational methods confront challenges in selecting small gene subset. Methods: In our study, we propose a novel algorithm PSONRS_KNN for gene selection based on the particle swarm optimization (PSO) algorithm along with the neighborhood rough set (NRS) reduction model and the K-nearest neighborhood (KNN) classifier. Results: First, the top-ranked candidate genes are obtained by the GainRatioAttributeEval preselection algorithm in WEKA. Then, the minimum possible meaningful set of genes is selected by combining PSO with NRS and KNN classifier. Conclusion: Experimental results on five microarray gene expression datasets demonstrate that the performance of the proposed method is better than existing state-of-the-art methods in terms of classification accuracy and the number of selected genes.

scholarly journals A Gene selection approach based on the fisher linear discriminant and the neighborhood rough set

Bioengineered ◽  
2017 ◽  
Vol 9 (1) ◽  
pp. 144-151 ◽  
Author(s):  
Lin Sun ◽  
Xiaoyu Zhang ◽  
Jiucheng Xu ◽  
Wei Wang ◽  
Ruonan Liu
Keyword(s):  
Rough Set ◽  
Gene Selection ◽  
Fisher Linear Discriminant ◽  
Linear Discriminant ◽  
Selection Approach ◽  
Neighborhood Rough Set

scholarly journals Neighborhood Rough Set Reduction-Based Gene Selection and Prioritization for Gene Expression Profile Analysis and Molecular Cancer Classification

2010 ◽  
Vol 2010 ◽  
pp. 1-12 ◽  
Author(s):  
Mei-Ling Hou ◽  
Shu-Lin Wang ◽  
Xue-Ling Li ◽  
Ying-Ke Lei
Keyword(s):  
Gene Expression ◽  
Gene Expression Profile ◽  
Expression Profile ◽  
Rough Set ◽  
Gene Selection ◽  
Cancer Biomarkers ◽  
Cancer Classification ◽  
Tumor Classification ◽  
Cancer Type ◽  
Neighborhood Rough Set

Selection of reliable cancer biomarkers is crucial for gene expression profile-based precise diagnosis of cancer type and successful treatment. However, current studies are confronted with overfitting and dimensionality curse in tumor classification and false positives in the identification of cancer biomarkers. Here, we developed a novel gene-ranking method based on neighborhood rough set reduction for molecular cancer classification based on gene expression profile. Comparison with other methods such as PAM, ClaNC, Kruskal-Wallis rank sum test, and Relief-F, our method shows that only few top-ranked genes could achieve higher tumor classification accuracy. Moreover, although the selected genes are not typical of known oncogenes, they are found to play a crucial role in the occurrence of tumor through searching the scientific literature and analyzing protein interaction partners, which may be used as candidate cancer biomarkers.

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