Predicting transcription factor binding sites using local over-representation and comparative genomics
- Location
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Deutsche Nationalbibliothek Frankfurt am Main
- ISSN
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1471-2105
- Extent
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Online-Ressource
- Language
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Englisch
- Notes
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online resource.
- Bibliographic citation
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Predicting transcription factor binding sites using local over-representation and comparative genomics ; volume:7 ; number:1 ; day:31 ; month:8 ; year:2006 ; pages:1-11 ; date:12.2006
BMC bioinformatics ; 7, Heft 1 (31.8.2006), 1-11, 12.2006
- Creator
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Defrance, Matthieu
- Contributor
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Touzet, Hélène
SpringerLink (Online service)
- DOI
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10.1186/1471-2105-7-396
- URN
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urn:nbn:de:1111-2016021625891
- Rights
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Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Last update
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15.08.2025, 7:30 AM CEST
Data provider
This object is provided by:
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Deutsche Nationalbibliothek. If you have any questions about the object, please contact the data provider.
Associated
- Defrance, Matthieu
- Touzet, Hélène
- SpringerLink (Online service)
Other Objects (12)
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Ab initio identification of putative human transcription factor binding sites by comparative genomics
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Virtual ChIP-seq: predicting transcription factor binding by learning from the transcriptome
Predicting expression: the complementary power of histone modification and transcription factor binding data
Principal component analysis for predicting transcription-factor binding motifs from array-derived data
Combining comparative genomics with de novo motif discovery to identify human transcription factor DNA-binding motifs
Predicting nuclear G-quadruplex RNA-binding proteins with roles in transcription and phase separation
Inferring functional transcription factor-gene binding pairs by integrating transcription factor binding data with transcription factor knockout data
Predicting transcription factor binding using ensemble random forest models [version 2; peer review: 2 approved]
Predicting splicing patterns from the transcription factor binding sites in the promoter with deep learning
Statistics for transcription factor binding sites
Recurrent Neural Network for Predicting Transcription Factor Binding Sites
Ab initio identification of putative human transcription factor binding sites by comparative genomics
Virtual ChIP-seq: Predicting transcription factor binding by learning from the transcriptome
Virtual ChIP-seq: predicting transcription factor binding by learning from the transcriptome
Predicting expression: the complementary power of histone modification and transcription factor binding data
Principal component analysis for predicting transcription-factor binding motifs from array-derived data
Combining comparative genomics with de novo motif discovery to identify human transcription factor DNA-binding motifs
Predicting nuclear G-quadruplex RNA-binding proteins with roles in transcription and phase separation
Inferring functional transcription factor-gene binding pairs by integrating transcription factor binding data with transcription factor knockout data
Predicting transcription factor binding using ensemble random forest models [version 2; peer review: 2 approved]
Predicting splicing patterns from the transcription factor binding sites in the promoter with deep learning
Statistics for transcription factor binding sites
Recurrent Neural Network for Predicting Transcription Factor Binding Sites
Ab initio identification of putative human transcription factor binding sites by comparative genomics
Virtual ChIP-seq: Predicting transcription factor binding by learning from the transcriptome
Virtual ChIP-seq: predicting transcription factor binding by learning from the transcriptome
Predicting expression: the complementary power of histone modification and transcription factor binding data
Principal component analysis for predicting transcription-factor binding motifs from array-derived data
Combining comparative genomics with de novo motif discovery to identify human transcription factor DNA-binding motifs
Predicting nuclear G-quadruplex RNA-binding proteins with roles in transcription and phase separation
Inferring functional transcription factor-gene binding pairs by integrating transcription factor binding data with transcription factor knockout data
Predicting transcription factor binding using ensemble random forest models [version 2; peer review: 2 approved]
Predicting splicing patterns from the transcription factor binding sites in the promoter with deep learning