Computational localization of transcription factor binding sites using extreme learning machines
Abstract Computational localization of transcription factor binding sites (TFBSs, also termed as motif instances) in DNA sequences greatly helps biologists in saving experimental cost and time for motif discovery. The task can be formulated as feature-based object location identification problem, wh...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Wang, Dianhui [verfasserIn] |
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| Format: |
Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2012 |
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| Schlagwörter: |
Feature-based location prediction |
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| Anmerkung: |
© Springer-Verlag 2012 |
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| Übergeordnetes Werk: |
Enthalten in: Soft computing - Springer-Verlag, 1997, 16(2012), 9 vom: 10. Feb., Seite 1595-1606 |
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| Übergeordnetes Werk: |
volume:16 ; year:2012 ; number:9 ; day:10 ; month:02 ; pages:1595-1606 |
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| DOI / URN: |
10.1007/s00500-012-0820-x |
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OLC203487238X |
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| 520 | |a Abstract Computational localization of transcription factor binding sites (TFBSs, also termed as motif instances) in DNA sequences greatly helps biologists in saving experimental cost and time for motif discovery. The task can be formulated as feature-based object location identification problem, which is remarkably different from traditional pattern recognition tasks. This paper aims to develop a machine learning approach for TFBSs location prediction through feature-based classifiers. Some specific features are extracted to characterize and distinguish the TFBSs from random k-mers. Then, a sampling technique is employed to generate dummy positives in the feature space for achieving better prediction performance. Three learner models are examined and a simple ensemble method is adopted in our classifiers design. Experimental results on eight benchmark datasets demonstrate that our proposed techniques have good potential for conserved motif detections. Comparative studies indicate that the extreme learning machine-based ensemble classifier outperforms the other learner models in terms of overall prediction accuracy and computational complexity. | ||
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10.1007/s00500-012-0820-x doi (DE-627)OLC203487238X (DE-He213)s00500-012-0820-x-p DE-627 ger DE-627 rakwb eng 004 VZ 004 VZ 11 ssgn Wang, Dianhui verfasserin aut Computational localization of transcription factor binding sites using extreme learning machines 2012 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag 2012 Abstract Computational localization of transcription factor binding sites (TFBSs, also termed as motif instances) in DNA sequences greatly helps biologists in saving experimental cost and time for motif discovery. The task can be formulated as feature-based object location identification problem, which is remarkably different from traditional pattern recognition tasks. This paper aims to develop a machine learning approach for TFBSs location prediction through feature-based classifiers. Some specific features are extracted to characterize and distinguish the TFBSs from random k-mers. Then, a sampling technique is employed to generate dummy positives in the feature space for achieving better prediction performance. Three learner models are examined and a simple ensemble method is adopted in our classifiers design. Experimental results on eight benchmark datasets demonstrate that our proposed techniques have good potential for conserved motif detections. Comparative studies indicate that the extreme learning machine-based ensemble classifier outperforms the other learner models in terms of overall prediction accuracy and computational complexity. DNA motifs Feature-based location prediction Imbalanced data classification Extreme learning machine ensembles Do, Hai Thanh aut Enthalten in Soft computing Springer-Verlag, 1997 16(2012), 9 vom: 10. Feb., Seite 1595-1606 (DE-627)231970536 (DE-600)1387526-7 (DE-576)060238259 1432-7643 nnns volume:16 year:2012 number:9 day:10 month:02 pages:1595-1606 https://doi.org/10.1007/s00500-012-0820-x lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_40 GBV_ILN_70 GBV_ILN_267 GBV_ILN_2018 GBV_ILN_4277 AR 16 2012 9 10 02 1595-1606 |
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10.1007/s00500-012-0820-x doi (DE-627)OLC203487238X (DE-He213)s00500-012-0820-x-p DE-627 ger DE-627 rakwb eng 004 VZ 004 VZ 11 ssgn Wang, Dianhui verfasserin aut Computational localization of transcription factor binding sites using extreme learning machines 2012 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag 2012 Abstract Computational localization of transcription factor binding sites (TFBSs, also termed as motif instances) in DNA sequences greatly helps biologists in saving experimental cost and time for motif discovery. The task can be formulated as feature-based object location identification problem, which is remarkably different from traditional pattern recognition tasks. This paper aims to develop a machine learning approach for TFBSs location prediction through feature-based classifiers. Some specific features are extracted to characterize and distinguish the TFBSs from random k-mers. Then, a sampling technique is employed to generate dummy positives in the feature space for achieving better prediction performance. Three learner models are examined and a simple ensemble method is adopted in our classifiers design. Experimental results on eight benchmark datasets demonstrate that our proposed techniques have good potential for conserved motif detections. Comparative studies indicate that the extreme learning machine-based ensemble classifier outperforms the other learner models in terms of overall prediction accuracy and computational complexity. DNA motifs Feature-based location prediction Imbalanced data classification Extreme learning machine ensembles Do, Hai Thanh aut Enthalten in Soft computing Springer-Verlag, 1997 16(2012), 9 vom: 10. Feb., Seite 1595-1606 (DE-627)231970536 (DE-600)1387526-7 (DE-576)060238259 1432-7643 nnns volume:16 year:2012 number:9 day:10 month:02 pages:1595-1606 https://doi.org/10.1007/s00500-012-0820-x lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_40 GBV_ILN_70 GBV_ILN_267 GBV_ILN_2018 GBV_ILN_4277 AR 16 2012 9 10 02 1595-1606 |
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10.1007/s00500-012-0820-x doi (DE-627)OLC203487238X (DE-He213)s00500-012-0820-x-p DE-627 ger DE-627 rakwb eng 004 VZ 004 VZ 11 ssgn Wang, Dianhui verfasserin aut Computational localization of transcription factor binding sites using extreme learning machines 2012 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag 2012 Abstract Computational localization of transcription factor binding sites (TFBSs, also termed as motif instances) in DNA sequences greatly helps biologists in saving experimental cost and time for motif discovery. The task can be formulated as feature-based object location identification problem, which is remarkably different from traditional pattern recognition tasks. This paper aims to develop a machine learning approach for TFBSs location prediction through feature-based classifiers. Some specific features are extracted to characterize and distinguish the TFBSs from random k-mers. Then, a sampling technique is employed to generate dummy positives in the feature space for achieving better prediction performance. Three learner models are examined and a simple ensemble method is adopted in our classifiers design. Experimental results on eight benchmark datasets demonstrate that our proposed techniques have good potential for conserved motif detections. Comparative studies indicate that the extreme learning machine-based ensemble classifier outperforms the other learner models in terms of overall prediction accuracy and computational complexity. DNA motifs Feature-based location prediction Imbalanced data classification Extreme learning machine ensembles Do, Hai Thanh aut Enthalten in Soft computing Springer-Verlag, 1997 16(2012), 9 vom: 10. Feb., Seite 1595-1606 (DE-627)231970536 (DE-600)1387526-7 (DE-576)060238259 1432-7643 nnns volume:16 year:2012 number:9 day:10 month:02 pages:1595-1606 https://doi.org/10.1007/s00500-012-0820-x lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_40 GBV_ILN_70 GBV_ILN_267 GBV_ILN_2018 GBV_ILN_4277 AR 16 2012 9 10 02 1595-1606 |
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Abstract Computational localization of transcription factor binding sites (TFBSs, also termed as motif instances) in DNA sequences greatly helps biologists in saving experimental cost and time for motif discovery. The task can be formulated as feature-based object location identification problem, which is remarkably different from traditional pattern recognition tasks. This paper aims to develop a machine learning approach for TFBSs location prediction through feature-based classifiers. Some specific features are extracted to characterize and distinguish the TFBSs from random k-mers. Then, a sampling technique is employed to generate dummy positives in the feature space for achieving better prediction performance. Three learner models are examined and a simple ensemble method is adopted in our classifiers design. Experimental results on eight benchmark datasets demonstrate that our proposed techniques have good potential for conserved motif detections. Comparative studies indicate that the extreme learning machine-based ensemble classifier outperforms the other learner models in terms of overall prediction accuracy and computational complexity. © Springer-Verlag 2012 |
| abstractGer |
Abstract Computational localization of transcription factor binding sites (TFBSs, also termed as motif instances) in DNA sequences greatly helps biologists in saving experimental cost and time for motif discovery. The task can be formulated as feature-based object location identification problem, which is remarkably different from traditional pattern recognition tasks. This paper aims to develop a machine learning approach for TFBSs location prediction through feature-based classifiers. Some specific features are extracted to characterize and distinguish the TFBSs from random k-mers. Then, a sampling technique is employed to generate dummy positives in the feature space for achieving better prediction performance. Three learner models are examined and a simple ensemble method is adopted in our classifiers design. Experimental results on eight benchmark datasets demonstrate that our proposed techniques have good potential for conserved motif detections. Comparative studies indicate that the extreme learning machine-based ensemble classifier outperforms the other learner models in terms of overall prediction accuracy and computational complexity. © Springer-Verlag 2012 |
| abstract_unstemmed |
Abstract Computational localization of transcription factor binding sites (TFBSs, also termed as motif instances) in DNA sequences greatly helps biologists in saving experimental cost and time for motif discovery. The task can be formulated as feature-based object location identification problem, which is remarkably different from traditional pattern recognition tasks. This paper aims to develop a machine learning approach for TFBSs location prediction through feature-based classifiers. Some specific features are extracted to characterize and distinguish the TFBSs from random k-mers. Then, a sampling technique is employed to generate dummy positives in the feature space for achieving better prediction performance. Three learner models are examined and a simple ensemble method is adopted in our classifiers design. Experimental results on eight benchmark datasets demonstrate that our proposed techniques have good potential for conserved motif detections. Comparative studies indicate that the extreme learning machine-based ensemble classifier outperforms the other learner models in terms of overall prediction accuracy and computational complexity. © Springer-Verlag 2012 |
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Computational localization of transcription factor binding sites using extreme learning machines |
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https://doi.org/10.1007/s00500-012-0820-x |
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false |
| author2 |
Do, Hai Thanh |
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Do, Hai Thanh |
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231970536 |
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| doi_str |
10.1007/s00500-012-0820-x |
| up_date |
2024-07-03T22:48:08.041Z |
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1803599885675528192 |
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