Predicting protein-binding regions in RNA using nucleotide profiles and compositions
- Location
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Deutsche Nationalbibliothek Frankfurt am Main
- ISSN
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1752-0509
- Extent
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Online-Ressource
- Language
-
Englisch
- Notes
-
online resource.
- Bibliographic citation
-
Predicting protein-binding regions in RNA using nucleotide profiles and compositions ; volume:11 ; number:2 ; day:14 ; month:3 ; year:2017 ; pages:1-12 ; date:3.2017
BMC systems biology ; 11, Heft 2 (14.3.2017), 1-12, 3.2017
- Classification
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Biowissenschaften, Biologie
- Creator
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Choi, Daesik
- Contributor
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Park, Byungkyu
Chae, Hanju
Lee, Wook
Han, Kyungsook
SpringerLink (Online service)
- DOI
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10.1186/s12918-017-0386-4
- URN
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urn:nbn:de:1111-201704045636
- Rights
-
Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Last update
-
14.08.2025, 11:00 AM CEST
Data provider
This object is provided by:
Deutsche Nationalbibliothek. If you have any questions about the object, please contact the data provider.
Deutsche Nationalbibliothek. If you have any questions about the object, please contact the data provider.
Associated
- Choi, Daesik
- Park, Byungkyu
- Chae, Hanju
- Lee, Wook
- Han, Kyungsook
- SpringerLink (Online service)
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A study on structural dynamics of a cyclic nucleotide-binding domain during ligand binding
Efficient Late Binding of Dynamic Function Compositions
PvGAMA reticulocyte binding activity: predicting conserved functional regions by natural selection analysis
Predicting sequence and structural specificities of RNA binding regions recognized by splicing factor SRSF1
Structural snapshot of cyclic nucleotide binding domains from cyclic nucleotide-sensitive ion channels
Solid-state 31P NMR of nucleotide binding proteins
Solid-state 31P NMR of nucleotide binding proteins
SNVHMM: predicting single nucleotide variants from next generation sequencing
Evolution of allostery in the cyclic nucleotide binding module
DVA: predicting the functional impact of single nucleotide missense variants
Predicting microbial community compositions in wastewater treatment plants using artificial neural networks
Predicting osteoporosis with body compositions in postmenopausal women: a non-invasive method