Deep learning architectures for prediction of nucleosome positioning from sequences data
- 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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Deep learning architectures for prediction of nucleosome positioning from sequences data ; volume:19 ; number:14 ; day:20 ; month:11 ; year:2018 ; pages:127-135 ; date:11.2018
BMC bioinformatics ; 19, Heft 14 (20.11.2018), 127-135, 11.2018
- Classification
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Biowissenschaften, Biologie
- Creator
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Di Gangi, Mattia
Lo Bosco, Giosuè
Rizzo, Riccardo
- Contributor
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SpringerLink (Online service)
- DOI
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10.1186/s12859-018-2386-9
- URN
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urn:nbn:de:101:1-2019010103223558364030
- Rights
-
Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Last update
-
14.08.2025, 11:00 AM CEST
Data provider
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Deutsche Nationalbibliothek. If you have any questions about the object, please contact the data provider.
Associated
- Di Gangi, Mattia
- Lo Bosco, Giosuè
- Rizzo, Riccardo
- SpringerLink (Online service)
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Absolute nucleosome occupancy and reconstitution of nucleosome positioning mechanisms for Saccharomyces cerevisiae
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An in vitro-identified high-affinity nucleosome-positioning signal is capable of transiently positioning a nucleosome in vivo
Nucleosome positioning and remodeling mechanisms in Saccharomyces cerevisiae
Insights into distinct regulatory modes of nucleosome positioning
Transcriptional interaction-assisted identification of dynamic nucleosome positioning
Sequences of Software Architectures
Comparative analysis and prediction of nucleosome positioning using integrative feature representation and machine learning algorithms
N 6-methyldeoxyadenosine directs nucleosome positioning in Tetrahymena DNA
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Prediction of nucleosome rotational positioning in yeast and human genomes based on sequence-dependent DNA anisotropy
Evolution and biophysics of nucleosome positioning
Absolute nucleosome occupancy and reconstitution of nucleosome positioning mechanisms for Saccharomyces cerevisiae
Chemical map-based prediction of nucleosome positioning using the Bioconductor package nuCpos
An in vitro-identified high-affinity nucleosome-positioning signal is capable of transiently positioning a nucleosome in vivo
Nucleosome positioning and remodeling mechanisms in Saccharomyces cerevisiae
Insights into distinct regulatory modes of nucleosome positioning
Transcriptional interaction-assisted identification of dynamic nucleosome positioning
Sequences of Software Architectures
Comparative analysis and prediction of nucleosome positioning using integrative feature representation and machine learning algorithms
N 6-methyldeoxyadenosine directs nucleosome positioning in Tetrahymena DNA
Predicting nucleosome positioning using a duration Hidden Markov Model