Abstract
Projection Reconstruction Nuclear Magnetic Resonance (PR-NMR) is a new technique to generate multi-dimensional NMR spectra, which have discrete features that are relatively sparsely distributed in space. A small number of projections from lower dimensional NMR spectra are used to reconstruct the multi-dimensional NMR spectra. We propose an efficient algorithm which employs a blocked Gibbs sampler to accurately reconstruct NMR spectra. This statistical method generates samples in Bayesian scheme. Our proposed algorithm is tested on a set of six projections derived from the three-dimensional 700 MHz HNCO spectrum of HasA, a 187-residue heme binding protein.
| Original language | English |
|---|---|
| Pages (from-to) | 940-947 |
| Number of pages | 8 |
| Journal | Journal of Machine Learning Research |
| Volume | 9 |
| Publication status | Published - 2010 |
| Externally published | Yes |
| Event | 13th International Conference on Artificial Intelligence and Statistics, AISTATS 2010 - Sardinia, Italy Duration: 2010 May 13 → 2010 May 15 |
ASJC Scopus subject areas
- Software
- Control and Systems Engineering
- Statistics and Probability
- Artificial Intelligence