Gene selection for cancer identification: a decision tree model empowered by particle swarm optimization algorithm
Background In the application of microarray data, how to select a small number of informative genes from thousands of genes that may contribute to the occurrence of cancers is an important issue. Many researchers use various computational intelligence methods to analyzed gene expression data. Result...
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| Published in | BMC bioinformatics Vol. 15; no. 1; p. 49 |
|---|---|
| Main Authors | , , , , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
London
BioMed Central
20.02.2014
BioMed Central Ltd Springer Nature B.V |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1471-2105 1471-2105 |
| DOI | 10.1186/1471-2105-15-49 |
Cover
| Abstract | Background
In the application of microarray data, how to select a small number of informative genes from thousands of genes that may contribute to the occurrence of cancers is an important issue. Many researchers use various computational intelligence methods to analyzed gene expression data.
Results
To achieve efficient gene selection from thousands of candidate genes that can contribute in identifying cancers, this study aims at developing a novel method utilizing particle swarm optimization combined with a decision tree as the classifier. This study also compares the performance of our proposed method with other well-known benchmark classification methods (support vector machine, self-organizing map, back propagation neural network, C4.5 decision tree, Naive Bayes, CART decision tree, and artificial immune recognition system) and conducts experiments on 11 gene expression cancer datasets.
Conclusion
Based on statistical analysis, our proposed method outperforms other popular classifiers for all test datasets, and is compatible to SVM for certain specific datasets. Further, the housekeeping genes with various expression patterns and tissue-specific genes are identified. These genes provide a high discrimination power on cancer classification. |
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| AbstractList | In the application of microarray data, how to select a small number of informative genes from thousands of genes that may contribute to the occurrence of cancers is an important issue. Many researchers use various computational intelligence methods to analyzed gene expression data.
To achieve efficient gene selection from thousands of candidate genes that can contribute in identifying cancers, this study aims at developing a novel method utilizing particle swarm optimization combined with a decision tree as the classifier. This study also compares the performance of our proposed method with other well-known benchmark classification methods (support vector machine, self-organizing map, back propagation neural network, C4.5 decision tree, Naive Bayes, CART decision tree, and artificial immune recognition system) and conducts experiments on 11 gene expression cancer datasets.
Based on statistical analysis, our proposed method outperforms other popular classifiers for all test datasets, and is compatible to SVM for certain specific datasets. Further, the housekeeping genes with various expression patterns and tissue-specific genes are identified. These genes provide a high discrimination power on cancer classification. In the application of microarray data, how to select a small number of informative genes from thousands of genes that may contribute to the occurrence of cancers is an important issue. Many researchers use various computational intelligence methods to analyzed gene expression data. To achieve efficient gene selection from thousands of candidate genes that can contribute in identifying cancers, this study aims at developing a novel method utilizing particle swarm optimization combined with a decision tree as the classifier. This study also compares the performance of our proposed method with other well-known benchmark classification methods (support vector machine, self-organizing map, back propagation neural network, C4.5 decision tree, Naive Bayes, CART decision tree, and artificial immune recognition system) and conducts experiments on 11 gene expression cancer datasets. Based on statistical analysis, our proposed method outperforms other popular classifiers for all test datasets, and is compatible to SVM for certain specific datasets. Further, the housekeeping genes with various expression patterns and tissue-specific genes are identified. These genes provide a high discrimination power on cancer classification. In the application of microarray data, how to select a small number of informative genes from thousands of genes that may contribute to the occurrence of cancers is an important issue. Many researchers use various computational intelligence methods to analyzed gene expression data.BACKGROUNDIn the application of microarray data, how to select a small number of informative genes from thousands of genes that may contribute to the occurrence of cancers is an important issue. Many researchers use various computational intelligence methods to analyzed gene expression data.To achieve efficient gene selection from thousands of candidate genes that can contribute in identifying cancers, this study aims at developing a novel method utilizing particle swarm optimization combined with a decision tree as the classifier. This study also compares the performance of our proposed method with other well-known benchmark classification methods (support vector machine, self-organizing map, back propagation neural network, C4.5 decision tree, Naive Bayes, CART decision tree, and artificial immune recognition system) and conducts experiments on 11 gene expression cancer datasets.RESULTSTo achieve efficient gene selection from thousands of candidate genes that can contribute in identifying cancers, this study aims at developing a novel method utilizing particle swarm optimization combined with a decision tree as the classifier. This study also compares the performance of our proposed method with other well-known benchmark classification methods (support vector machine, self-organizing map, back propagation neural network, C4.5 decision tree, Naive Bayes, CART decision tree, and artificial immune recognition system) and conducts experiments on 11 gene expression cancer datasets.Based on statistical analysis, our proposed method outperforms other popular classifiers for all test datasets, and is compatible to SVM for certain specific datasets. Further, the housekeeping genes with various expression patterns and tissue-specific genes are identified. These genes provide a high discrimination power on cancer classification.CONCLUSIONBased on statistical analysis, our proposed method outperforms other popular classifiers for all test datasets, and is compatible to SVM for certain specific datasets. Further, the housekeeping genes with various expression patterns and tissue-specific genes are identified. These genes provide a high discrimination power on cancer classification. Doc number: 49 Abstract Background: In the application of microarray data, how to select a small number of informative genes from thousands of genes that may contribute to the occurrence of cancers is an important issue. Many researchers use various computational intelligence methods to analyzed gene expression data. Results: To achieve efficient gene selection from thousands of candidate genes that can contribute in identifying cancers, this study aims at developing a novel method utilizing particle swarm optimization combined with a decision tree as the classifier. This study also compares the performance of our proposed method with other well-known benchmark classification methods (support vector machine, self-organizing map, back propagation neural network, C4.5 decision tree, Naive Bayes, CART decision tree, and artificial immune recognition system) and conducts experiments on 11 gene expression cancer datasets. Conclusion: Based on statistical analysis, our proposed method outperforms other popular classifiers for all test datasets, and is compatible to SVM for certain specific datasets. Further, the housekeeping genes with various expression patterns and tissue-specific genes are identified. These genes provide a high discrimination power on cancer classification. Background In the application of microarray data, how to select a small number of informative genes from thousands of genes that may contribute to the occurrence of cancers is an important issue. Many researchers use various computational intelligence methods to analyzed gene expression data. Results To achieve efficient gene selection from thousands of candidate genes that can contribute in identifying cancers, this study aims at developing a novel method utilizing particle swarm optimization combined with a decision tree as the classifier. This study also compares the performance of our proposed method with other well-known benchmark classification methods (support vector machine, self-organizing map, back propagation neural network, C4.5 decision tree, Naive Bayes, CART decision tree, and artificial immune recognition system) and conducts experiments on 11 gene expression cancer datasets. Conclusion Based on statistical analysis, our proposed method outperforms other popular classifiers for all test datasets, and is compatible to SVM for certain specific datasets. Further, the housekeeping genes with various expression patterns and tissue-specific genes are identified. These genes provide a high discrimination power on cancer classification. Keywords: Gene expression, Cancer, Particle swarm optimization, Decision tree classifier Background: In the application of microarray data, how to select a small number of informative genes from thousands of genes that may contribute to the occurrence of cancers is an important issue. Many researchers use various computational intelligence methods to analyzed gene expression data. Results: To achieve efficient gene selection from thousands of candidate genes that can contribute in identifying cancers, this study aims at developing a novel method utilizing particle swarm optimization combined with a decision tree as the classifier. This study also compares the performance of our proposed method with other well-known benchmark classification methods (support vector machine, self-organizing map, back propagation neural network, C4.5 decision tree, Naive Bayes, CART decision tree, and artificial immune recognition system) and conducts experiments on 11 gene expression cancer datasets. Conclusion: Based on statistical analysis, our proposed method outperforms other popular classifiers for all test datasets, and is compatible to SVM for certain specific datasets. Further, the housekeeping genes with various expression patterns and tissue-specific genes are identified. These genes provide a high discrimination power on cancer classification. Background In the application of microarray data, how to select a small number of informative genes from thousands of genes that may contribute to the occurrence of cancers is an important issue. Many researchers use various computational intelligence methods to analyzed gene expression data. Results To achieve efficient gene selection from thousands of candidate genes that can contribute in identifying cancers, this study aims at developing a novel method utilizing particle swarm optimization combined with a decision tree as the classifier. This study also compares the performance of our proposed method with other well-known benchmark classification methods (support vector machine, self-organizing map, back propagation neural network, C4.5 decision tree, Naive Bayes, CART decision tree, and artificial immune recognition system) and conducts experiments on 11 gene expression cancer datasets. Conclusion Based on statistical analysis, our proposed method outperforms other popular classifiers for all test datasets, and is compatible to SVM for certain specific datasets. Further, the housekeeping genes with various expression patterns and tissue-specific genes are identified. These genes provide a high discrimination power on cancer classification. |
| ArticleNumber | 49 |
| Audience | Academic |
| Author | Tsai, Min-Lung Adrian, Angelia Melani Yang, Tzu-Sen Wang, Kung-Jeng Chen, Kun-Huang Chang, Ku-Shang Wang, Kung-Min Tan, Kuo-Pin Cheng, Wei-Chung Teng, Nai-Chia |
| AuthorAffiliation | 8 School of Dentistry, College of Oral Medicine, Taipei Medical University, Taipei, Taiwan, R.O.C 7 Taiwan Research Center for Biomedical Implants and Microsurgery Devices, Taipei Medical University Taipei 110, Taiwan, R.O.C 5 Genomic Research Center, National Yang-Ming University, Taipei 11221, Taiwan, R.O.C 1 Department of Industrial Management, National Taiwan University of Science and Technology, Taipei 106, Taiwan, R.O.C 3 Department of Surgery, Shin-Kong Wu Ho-Su Memorial Hospital, Taipei, Taiwan, R.O.C 6 School of Dental Technology, Taipei Medical University, Taipei 110, Taiwan, R.O.C 9 MBA, School of Management, National Taiwan University of Science and Technology, Taipei 106, Taiwan, R.O.C 4 Pediatric Neurosurgery, Department of Surgery, Cheng Hsin General Hospital, Taipei 11220, Taiwan, R.O.C 2 Department of Food Science, Yuanpei University, No. 306, Yuanpei Street, Hsinchu 300, Taiwan, R.O.C |
| AuthorAffiliation_xml | – name: 4 Pediatric Neurosurgery, Department of Surgery, Cheng Hsin General Hospital, Taipei 11220, Taiwan, R.O.C – name: 1 Department of Industrial Management, National Taiwan University of Science and Technology, Taipei 106, Taiwan, R.O.C – name: 8 School of Dentistry, College of Oral Medicine, Taipei Medical University, Taipei, Taiwan, R.O.C – name: 9 MBA, School of Management, National Taiwan University of Science and Technology, Taipei 106, Taiwan, R.O.C – name: 5 Genomic Research Center, National Yang-Ming University, Taipei 11221, Taiwan, R.O.C – name: 2 Department of Food Science, Yuanpei University, No. 306, Yuanpei Street, Hsinchu 300, Taiwan, R.O.C – name: 6 School of Dental Technology, Taipei Medical University, Taipei 110, Taiwan, R.O.C – name: 7 Taiwan Research Center for Biomedical Implants and Microsurgery Devices, Taipei Medical University Taipei 110, Taiwan, R.O.C – name: 3 Department of Surgery, Shin-Kong Wu Ho-Su Memorial Hospital, Taipei, Taiwan, R.O.C |
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| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/24555567$$D View this record in MEDLINE/PubMed |
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| Cites_doi | 10.1093/bioinformatics/btn253 10.1186/1471-2105-11-421 10.1080/01621459.1975.10479865 10.1109/ICNN.1995.488968 10.1016/j.ygeno.2011.04.011 10.1093/bioinformatics/btq207 10.1016/j.compbiolchem.2007.10.001 10.1016/j.patrec.2004.08.015 10.1016/S0014-5793(00)01772-5 10.1006/jmbi.2001.4580 10.1016/j.neunet.2005.07.002 10.1186/1471-2105-10-S4-S7 10.1037/h0072400 10.1016/j.eswa.2004.12.023 10.1093/nar/gkm368 10.1073/pnas.97.1.262 10.1016/S0004-3702(97)00043-X 10.1007/s00521-011-0632-4 10.1007/s10115-007-0114-2 10.1093/bioinformatics/btp107 10.1007/s00500-007-0272-x 10.1198/016214502753479248 10.1186/1471-2105-5-64 10.1093/nar/gkh563 10.1111/j.2517-6161.1974.tb00994.x 10.1186/1471-2105-10-S1-S21 10.1016/j.ygeno.2004.09.007 10.1093/bioinformatics/bts108 10.1093/bioinformatics/btg179 10.1126/science.286.5439.531 10.1109/TAP.2004.823969 |
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| Copyright | Chen et al.; licensee BioMed Central Ltd. 2014 COPYRIGHT 2014 BioMed Central Ltd. 2014 Chen et al.; licensee BioMed Central Ltd. 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 credited. Copyright © 2014 Chen et al.; licensee BioMed Central Ltd. 2014 Chen et al.; licensee BioMed Central Ltd. |
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| Keywords | Decision tree classifier Gene expression Particle swarm optimization Cancer |
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| PublicationDecade | 2010 |
| PublicationPlace | London |
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| PublicationTitle | BMC bioinformatics |
| PublicationTitleAbbrev | BMC Bioinformatics |
| PublicationTitleAlternate | BMC Bioinformatics |
| PublicationYear | 2014 |
| Publisher | BioMed Central BioMed Central Ltd Springer Nature B.V |
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| References | J Robinson (6322_CR17) 2004; 52 E Alba (6322_CR1) 2007; 9 R Kahavi (6322_CR5) 1997; 97 L Li (6322_CR15) 2005; 85 R Batuwita (6322_CR26) 2009; 25 S Geisser (6322_CR39) 1975; 70 J Kennedy (6322_CR16) 1995; 4 S Tan (6322_CR37) 2005; 28 L Li (6322_CR14) 2001; 4 Y Wang (6322_CR27) 2011; 98 F Mosteller (6322_CR42) 1963; 58 WC Cheng (6322_CR22) 2010; 11 Y Su (6322_CR4) 2003; 19 TOM laboratory (6322_CR34) 2013 Y Shi (6322_CR36) 1998 S Dudoit (6322_CR24) 2002; 97 C Cortes (6322_CR43) 1995; 20 I Kononenko (6322_CR44) 1995 S Li (6322_CR2) 2008; 12 P Jiang (6322_CR25) 2007; 35 MS Mohamad (6322_CR19) 2009 A Brazma (6322_CR31) 2000; 480 MP Brown (6322_CR8) 2000; 97 6322_CR23 TR Golub (6322_CR32) 1999; 286 X Li (6322_CR6) 2004; 32 S Hua (6322_CR10) 2001; 308 L Evers (6322_CR9) 2008; 24 L Nanni (6322_CR28) 2008; 28 I Park (6322_CR29) 2010; 26 A Ahmad (6322_CR3) 2005; 26 M Stone (6322_CR38) 1974; 36 LF Chen (6322_CR18) 2011; 21 Y Saeys (6322_CR12) 2004; 5 W Zhao (6322_CR33) 2011; 3 Q Shen (6322_CR20) 2008; 32 JH Oh (6322_CR11) 2009; 10 X Wu (6322_CR21) 2008; 14 XM Zhao (6322_CR7) 2005; 18 Y Zhu (6322_CR13) 2009; 10 PN Tan (6322_CR30) 2005 J Kennedy (6322_CR35) 2001 S Larson (6322_CR40) 1931; 22 F Mosteller (6322_CR41) 1968 |
| References_xml | – volume: 24 start-page: 1632 year: 2008 ident: 6322_CR9 publication-title: Bioinformatics doi: 10.1093/bioinformatics/btn253 – volume: 3 start-page: 184 year: 2011 ident: 6322_CR33 publication-title: Int J Adv Comput Technol – volume: 11 start-page: 421 year: 2010 ident: 6322_CR22 publication-title: BMC Bioinforma doi: 10.1186/1471-2105-11-421 – volume: 70 start-page: 320 year: 1975 ident: 6322_CR39 publication-title: J Am Stat Assoc doi: 10.1080/01621459.1975.10479865 – start-page: 762 volume-title: Particle swarm optimization for gene selection in classifying cancer classes year: 2009 ident: 6322_CR19 – volume: 4 start-page: 1942 year: 1995 ident: 6322_CR16 publication-title: IEEE Int Conf Neural Networks - Conf Proc doi: 10.1109/ICNN.1995.488968 – volume: 98 start-page: 73 year: 2011 ident: 6322_CR27 publication-title: Genomics doi: 10.1016/j.ygeno.2011.04.011 – volume: 26 start-page: 1506 year: 2010 ident: 6322_CR29 publication-title: Bioinformatics doi: 10.1093/bioinformatics/btq207 – volume: 32 start-page: 52 year: 2008 ident: 6322_CR20 publication-title: Comput Biol Chem doi: 10.1016/j.compbiolchem.2007.10.001 – volume: 26 start-page: 43 year: 2005 ident: 6322_CR3 publication-title: Pattern Recogn Lett doi: 10.1016/j.patrec.2004.08.015 – volume: 480 start-page: 2 year: 2000 ident: 6322_CR31 publication-title: FEBS Lett doi: 10.1016/S0014-5793(00)01772-5 – volume: 58 start-page: 275 year: 1963 ident: 6322_CR42 publication-title: J Am Stat Assoc – volume: 9 start-page: 284 year: 2007 ident: 6322_CR1 publication-title: IEEE C Evol Computat – volume: 308 start-page: 397 year: 2001 ident: 6322_CR10 publication-title: J Mol Biol doi: 10.1006/jmbi.2001.4580 – volume: 18 start-page: 1019 year: 2005 ident: 6322_CR7 publication-title: Neural Netw doi: 10.1016/j.neunet.2005.07.002 – start-page: 69 volume-title: A Modified Particle Swarm Optimizer year: 1998 ident: 6322_CR36 – volume: 20 start-page: 273 year: 1995 ident: 6322_CR43 publication-title: Mach Learn – volume: 10 start-page: S7 year: 2009 ident: 6322_CR11 publication-title: BMC Bioinforma doi: 10.1186/1471-2105-10-S4-S7 – ident: 6322_CR23 – volume: 22 start-page: 45 year: 1931 ident: 6322_CR40 publication-title: J Educat Psychol doi: 10.1037/h0072400 – volume-title: TOM laboratory year: 2013 ident: 6322_CR34 – volume: 28 start-page: 667 year: 2005 ident: 6322_CR37 publication-title: Expert Syst Appl doi: 10.1016/j.eswa.2004.12.023 – volume: 35 start-page: W339 year: 2007 ident: 6322_CR25 publication-title: Nucleic Acids Res doi: 10.1093/nar/gkm368 – volume-title: Data analysis, including statistics. Handbook of Social Psychology year: 1968 ident: 6322_CR41 – volume: 97 start-page: 262 year: 2000 ident: 6322_CR8 publication-title: Proc Natl Acad Sci U S A doi: 10.1073/pnas.97.1.262 – volume: 97 start-page: 273 year: 1997 ident: 6322_CR5 publication-title: Artif Intell doi: 10.1016/S0004-3702(97)00043-X – volume: 21 start-page: 2087 issue: 8 year: 2011 ident: 6322_CR18 publication-title: Neural Comput Appl doi: 10.1007/s00521-011-0632-4 – volume: 14 start-page: 1 year: 2008 ident: 6322_CR21 publication-title: Knowl Inf Syst doi: 10.1007/s10115-007-0114-2 – volume: 25 start-page: 989 year: 2009 ident: 6322_CR26 publication-title: Bioinformatics doi: 10.1093/bioinformatics/btp107 – volume: 12 start-page: 1039 year: 2008 ident: 6322_CR2 publication-title: Soft Comput doi: 10.1007/s00500-007-0272-x – volume: 97 start-page: 77 year: 2002 ident: 6322_CR24 publication-title: J Am Stat Assoc doi: 10.1198/016214502753479248 – volume: 5 start-page: 64 year: 2004 ident: 6322_CR12 publication-title: BMC Bioinforma doi: 10.1186/1471-2105-5-64 – volume: 4 start-page: 727 year: 2001 ident: 6322_CR14 publication-title: Comb Chem High T Scr – volume: 32 start-page: 2685 year: 2004 ident: 6322_CR6 publication-title: Nucleic Acids Res doi: 10.1093/nar/gkh563 – volume-title: Michael Steinbach, and Vipin Kumar. Introduction to Data Mining year: 2005 ident: 6322_CR30 – volume: 36 start-page: 111 year: 1974 ident: 6322_CR38 publication-title: J Royal Stat Soc doi: 10.1111/j.2517-6161.1974.tb00994.x – volume: 10 start-page: S21 year: 2009 ident: 6322_CR13 publication-title: BMC Bioinforma doi: 10.1186/1471-2105-10-S1-S21 – start-page: 31 volume-title: A counter example to the stronger version of the binary tree hypothesis year: 1995 ident: 6322_CR44 – volume-title: Swarm Intelligence year: 2001 ident: 6322_CR35 – volume: 85 start-page: 16 year: 2005 ident: 6322_CR15 publication-title: Genomics doi: 10.1016/j.ygeno.2004.09.007 – volume: 28 start-page: 1151 year: 2008 ident: 6322_CR28 publication-title: Bioinformatics doi: 10.1093/bioinformatics/bts108 – volume: 19 start-page: 1578 year: 2003 ident: 6322_CR4 publication-title: Bioinformatics doi: 10.1093/bioinformatics/btg179 – volume: 286 start-page: 531 year: 1999 ident: 6322_CR32 publication-title: Science doi: 10.1126/science.286.5439.531 – volume: 52 start-page: 397 year: 2004 ident: 6322_CR17 publication-title: IEEE Trans Antennas Propag doi: 10.1109/TAP.2004.823969 |
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In the application of microarray data, how to select a small number of informative genes from thousands of genes that may contribute to the... In the application of microarray data, how to select a small number of informative genes from thousands of genes that may contribute to the occurrence of... Background In the application of microarray data, how to select a small number of informative genes from thousands of genes that may contribute to the... Doc number: 49 Abstract Background: In the application of microarray data, how to select a small number of informative genes from thousands of genes that may... Background: In the application of microarray data, how to select a small number of informative genes from thousands of genes that may contribute to the... |
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| SubjectTerms | Accuracy Algorithms Artificial Intelligence Atoms & subatomic particles Bayes Theorem Bioinformatics Biomedical and Life Sciences Cancer Cloning Computational Biology - methods Computational Biology/Bioinformatics Computer Appl. in Life Sciences Databases, Factual Decision Trees Deoxyribonucleic acid DNA Female Gene expression Gene Expression Profiling - methods Hospitals Humans Life Sciences Male Medical research Methodology Methodology Article Microarrays Neoplasms - classification Neoplasms - genetics Neoplasms - metabolism Neural networks Reproducibility of Results Science Statistical analysis Statistical methods Studies Support Vector Machine Transcriptome analysis Tumors |
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| Title | Gene selection for cancer identification: a decision tree model empowered by particle swarm optimization algorithm |
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