B-factor profile prediction for RNA flexibility using support vector machines
© 2017 Wiley Periodicals, Inc.
| Veröffentlicht in: | Journal of computational chemistry. - 1984. - 39(2018), 8 vom: 30. März, Seite 407-411 |
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| 1. Verfasser: | |
| Weitere Verfasser: | , , , |
| Format: | Online-Aufsatz |
| Sprache: | English |
| Veröffentlicht: |
2018
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| Zugriff auf das übergeordnete Werk: | Journal of computational chemistry |
| Schlagworte: | Journal Article Research Support, Non-U.S. Gov't RNA flexibility support vectors regression temperature B-factor RNA, Ribosomal |
| Zusammenfassung: | © 2017 Wiley Periodicals, Inc. Determining the flexibility of structured biomolecules is important for understanding their biological functions. One quantitative measurement of flexibility is the atomic Debye-Waller factor or temperature B-factor. Most existing studies are limited to temperature B-factors of proteins and their prediction. Only one method attempted to predict temperature B-factors of ribosomal RNA. Here, we developed and compared machine-learning techniques in prediction of temperature B-factors of RNAs. The best model based on Support Vector Machines yields Pearson's correction coefficient at 0.51 for fivefold cross validation and 0.50 for the independent test. Analysis of the performance indicates that the model has the best performance on rRNAs, tRNAs, and protein-bound RNAs, for long chains in particular. The server is available at http://sparks-lab.org/server/RNAflex. © 2017 Wiley Periodicals, Inc |
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| Beschreibung: | Date Completed 18.09.2019 Date Revised 18.09.2019 published: Print-Electronic Citation Status MEDLINE |
| ISSN: | 1096-987X |
| DOI: | 10.1002/jcc.25124 |