Feature selection of gene expression data for Cancer classification using double RBF-kernels
Abstract Background Using knowledge-based interpretation to analyze omics data can not only obtain essential information regarding various biological processes, but also reflect the current physiological status of cells and tissue. The major challenge to analyze gene expression data, with a large nu...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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BMC
2018-10-01
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| Series: | BMC Bioinformatics |
| Subjects: | |
| Online Access: | http://link.springer.com/article/10.1186/s12859-018-2400-2 |
| _version_ | 1828351075302768640 |
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| author | Shenghui Liu Chunrui Xu Yusen Zhang Jiaguo Liu Bin Yu Xiaoping Liu Matthias Dehmer |
| author_facet | Shenghui Liu Chunrui Xu Yusen Zhang Jiaguo Liu Bin Yu Xiaoping Liu Matthias Dehmer |
| author_sort | Shenghui Liu |
| collection | DOAJ |
| description | Abstract Background Using knowledge-based interpretation to analyze omics data can not only obtain essential information regarding various biological processes, but also reflect the current physiological status of cells and tissue. The major challenge to analyze gene expression data, with a large number of genes and small samples, is to extract disease-related information from a massive amount of redundant data and noise. Gene selection, eliminating redundant and irrelevant genes, has been a key step to address this problem. Results The modified method was tested on four benchmark datasets with either two-class phenotypes or multiclass phenotypes, outperforming previous methods, with relatively higher accuracy, true positive rate, false positive rate and reduced runtime. Conclusions This paper proposes an effective feature selection method, combining double RBF-kernels with weighted analysis, to extract feature genes from gene expression data, by exploring its nonlinear mapping ability. |
| first_indexed | 2024-04-14T01:34:18Z |
| format | Article |
| id | doaj.art-d5b8adeb799044f68d076ff1f29f471f |
| institution | Directory Open Access Journal |
| issn | 1471-2105 |
| language | English |
| last_indexed | 2024-04-14T01:34:18Z |
| publishDate | 2018-10-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Bioinformatics |
| spelling | doaj.art-d5b8adeb799044f68d076ff1f29f471f2022-12-22T02:20:03ZengBMCBMC Bioinformatics1471-21052018-10-0119111410.1186/s12859-018-2400-2Feature selection of gene expression data for Cancer classification using double RBF-kernelsShenghui Liu0Chunrui Xu1Yusen Zhang2Jiaguo Liu3Bin Yu4Xiaoping Liu5Matthias Dehmer6School of Mathematics and Statistics, Shandong University at WeihaiSchool of Mathematics and Statistics, Shandong University at WeihaiSchool of Mathematics and Statistics, Shandong University at WeihaiSchool of Mathematics and Statistics, Shandong University at WeihaiCollege of Mathematics and Physics, Qingdao University of Science and TechnologySchool of Mathematics and Statistics, Shandong University at WeihaiInstitute for Intelligent Production, Faculty for Management, University of Applied Sciences Upper Austria, Steyr CampusAbstract Background Using knowledge-based interpretation to analyze omics data can not only obtain essential information regarding various biological processes, but also reflect the current physiological status of cells and tissue. The major challenge to analyze gene expression data, with a large number of genes and small samples, is to extract disease-related information from a massive amount of redundant data and noise. Gene selection, eliminating redundant and irrelevant genes, has been a key step to address this problem. Results The modified method was tested on four benchmark datasets with either two-class phenotypes or multiclass phenotypes, outperforming previous methods, with relatively higher accuracy, true positive rate, false positive rate and reduced runtime. Conclusions This paper proposes an effective feature selection method, combining double RBF-kernels with weighted analysis, to extract feature genes from gene expression data, by exploring its nonlinear mapping ability.http://link.springer.com/article/10.1186/s12859-018-2400-2ClusteringGene expressionCancer classificationFeature selectionData mining |
| spellingShingle | Shenghui Liu Chunrui Xu Yusen Zhang Jiaguo Liu Bin Yu Xiaoping Liu Matthias Dehmer Feature selection of gene expression data for Cancer classification using double RBF-kernels BMC Bioinformatics Clustering Gene expression Cancer classification Feature selection Data mining |
| title | Feature selection of gene expression data for Cancer classification using double RBF-kernels |
| title_full | Feature selection of gene expression data for Cancer classification using double RBF-kernels |
| title_fullStr | Feature selection of gene expression data for Cancer classification using double RBF-kernels |
| title_full_unstemmed | Feature selection of gene expression data for Cancer classification using double RBF-kernels |
| title_short | Feature selection of gene expression data for Cancer classification using double RBF-kernels |
| title_sort | feature selection of gene expression data for cancer classification using double rbf kernels |
| topic | Clustering Gene expression Cancer classification Feature selection Data mining |
| url | http://link.springer.com/article/10.1186/s12859-018-2400-2 |
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