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Gene selection and classification for cancer microarray data based on machine learning and similarity measures
BACKGROUND: Microarray data have a high dimension of variables and a small sample size. In microarray data analyses, two important issues are how to choose genes, which provide reliable and good prediction for disease status, and how to determine the final gene set that is best for classification. A...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2011
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3287491/ https://www.ncbi.nlm.nih.gov/pubmed/22369383 http://dx.doi.org/10.1186/1471-2164-12-S5-S1 |
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author | Liu, Qingzhong Sung, Andrew H Chen, Zhongxue Liu, Jianzhong Chen, Lei Qiao, Mengyu Wang, Zhaohui Huang, Xudong Deng, Youping |
author_facet | Liu, Qingzhong Sung, Andrew H Chen, Zhongxue Liu, Jianzhong Chen, Lei Qiao, Mengyu Wang, Zhaohui Huang, Xudong Deng, Youping |
author_sort | Liu, Qingzhong |
collection | PubMed |
description | BACKGROUND: Microarray data have a high dimension of variables and a small sample size. In microarray data analyses, two important issues are how to choose genes, which provide reliable and good prediction for disease status, and how to determine the final gene set that is best for classification. Associations among genetic markers mean one can exploit information redundancy to potentially reduce classification cost in terms of time and money. RESULTS: To deal with redundant information and improve classification, we propose a gene selection method, Recursive Feature Addition, which combines supervised learning and statistical similarity measures. To determine the final optimal gene set for prediction and classification, we propose an algorithm, Lagging Prediction Peephole Optimization. By using six benchmark microarray gene expression data sets, we compared Recursive Feature Addition with recently developed gene selection methods: Support Vector Machine Recursive Feature Elimination, Leave-One-Out Calculation Sequential Forward Selection and several others. CONCLUSIONS: On average, with the use of popular learning machines including Nearest Mean Scaled Classifier, Support Vector Machine, Naive Bayes Classifier and Random Forest, Recursive Feature Addition outperformed other methods. Our studies also showed that Lagging Prediction Peephole Optimization is superior to random strategy; Recursive Feature Addition with Lagging Prediction Peephole Optimization obtained better testing accuracies than the gene selection method varSelRF. |
format | Online Article Text |
id | pubmed-3287491 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2011 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-32874912012-03-01 Gene selection and classification for cancer microarray data based on machine learning and similarity measures Liu, Qingzhong Sung, Andrew H Chen, Zhongxue Liu, Jianzhong Chen, Lei Qiao, Mengyu Wang, Zhaohui Huang, Xudong Deng, Youping BMC Genomics Research Article BACKGROUND: Microarray data have a high dimension of variables and a small sample size. In microarray data analyses, two important issues are how to choose genes, which provide reliable and good prediction for disease status, and how to determine the final gene set that is best for classification. Associations among genetic markers mean one can exploit information redundancy to potentially reduce classification cost in terms of time and money. RESULTS: To deal with redundant information and improve classification, we propose a gene selection method, Recursive Feature Addition, which combines supervised learning and statistical similarity measures. To determine the final optimal gene set for prediction and classification, we propose an algorithm, Lagging Prediction Peephole Optimization. By using six benchmark microarray gene expression data sets, we compared Recursive Feature Addition with recently developed gene selection methods: Support Vector Machine Recursive Feature Elimination, Leave-One-Out Calculation Sequential Forward Selection and several others. CONCLUSIONS: On average, with the use of popular learning machines including Nearest Mean Scaled Classifier, Support Vector Machine, Naive Bayes Classifier and Random Forest, Recursive Feature Addition outperformed other methods. Our studies also showed that Lagging Prediction Peephole Optimization is superior to random strategy; Recursive Feature Addition with Lagging Prediction Peephole Optimization obtained better testing accuracies than the gene selection method varSelRF. BioMed Central 2011-12-23 /pmc/articles/PMC3287491/ /pubmed/22369383 http://dx.doi.org/10.1186/1471-2164-12-S5-S1 Text en Copyright ©2011 Liu et al. licensee BioMed Central Ltd http://creativecommons.org/licenses/by/2.0 This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research Article Liu, Qingzhong Sung, Andrew H Chen, Zhongxue Liu, Jianzhong Chen, Lei Qiao, Mengyu Wang, Zhaohui Huang, Xudong Deng, Youping Gene selection and classification for cancer microarray data based on machine learning and similarity measures |
title | Gene selection and classification for cancer microarray data based on machine learning and similarity measures |
title_full | Gene selection and classification for cancer microarray data based on machine learning and similarity measures |
title_fullStr | Gene selection and classification for cancer microarray data based on machine learning and similarity measures |
title_full_unstemmed | Gene selection and classification for cancer microarray data based on machine learning and similarity measures |
title_short | Gene selection and classification for cancer microarray data based on machine learning and similarity measures |
title_sort | gene selection and classification for cancer microarray data based on machine learning and similarity measures |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3287491/ https://www.ncbi.nlm.nih.gov/pubmed/22369383 http://dx.doi.org/10.1186/1471-2164-12-S5-S1 |
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