Bootstrap Aggregation for Model Selection in the Model-free Formalism.
| Autor: | Crawley T; Department of Biochemistry and Molecular Biophysics, Columbia University, 630 West 168th Street, New York, NY 10032, United States., Palmer AG 3rd; Department of Biochemistry and Molecular Biophysics, Columbia University, 630 West 168th Street, New York, NY 10032, United States. |
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| Jazyk: | angličtina |
| Zdroj: | Magnetic resonance (Gottingen, Germany) [Magn Reson (Gott)] 2021; Vol. 2 (1), pp. 251-264. Date of Electronic Publication: 2021 May 05. |
| DOI: | 10.5194/mr-2-251-2021 |
| Abstrakt: | The ability to make robust inferences about the dynamics of biological macromolecules using NMR spectroscopy depends heavily on the application of appropriate theoretical models for nuclear spin relaxation. Data analysis for NMR laboratory-frame relaxation experiments typically involves selecting one of several model-free spectral density functions using a bias-corrected fitness test. Here, advances in statistical model selection theory, termed bootstrap aggregation or bagging, are applied to 15 N spin relaxation data, developing a multimodel inference solution to the model-free selection problem. The approach is illustrated using data sets recorded at four static magnetic fields for the bZip domain of the S. cerevisiae transcription factor GCN4. Competing Interests: Competing interests. The authors declare no competing interests. |
| Databáze: | MEDLINE |
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