| Title |
Application of Dirichlet process mixture model to the identification of spin systems in protein NMR spectra
|
|---|---|
| Published in |
Journal of Biomolecular NMR, May 2018
|
| DOI | 10.1007/s10858-018-0185-2 |
| Pubmed ID | |
| Authors |
Piotr Klukowski, Michał Augoff, Maciej Zamorski, Adam Gonczarek, Michał J. Walczak |
| Abstract |
Analysis of structure, function and interactions of proteins by NMR spectroscopy usually requires the assignment of resonances to the corresponding nuclei in protein. This task, although automated by methods such as FLYA or PINE, is still frequently performed manually. To facilitate the manual sequence-specific chemical shift assignment of complex proteins, we propose a method based on Dirichlet process mixture model (DPMM) that performs automated matching of groups of signals observed in NMR spectra to corresponding nuclei in protein sequence. The model has been extensively tested on 80 proteins retrieved from the BMRB database and has shown superior performance to the reference method. |
X Demographics
The data shown below were collected from the profiles of 3 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Canada | 1 | 33% |
| Spain | 1 | 33% |
| Unknown | 1 | 33% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 3 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 11 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 11 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 2 | 18% |
| Researcher | 2 | 18% |
| Student > Bachelor | 1 | 9% |
| Lecturer > Senior Lecturer | 1 | 9% |
| Student > Doctoral Student | 1 | 9% |
| Other | 1 | 9% |
| Unknown | 3 | 27% |
| Readers by discipline | Count | As % |
|---|---|---|
| Chemistry | 4 | 36% |
| Agricultural and Biological Sciences | 2 | 18% |
| Computer Science | 1 | 9% |
| Unknown | 4 | 36% |
Attention Score in Context
This research output has an Altmetric Attention Score of 3. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 04 July 2018.
All research outputs
#13,545,254
of 23,094,276 outputs
Outputs from Journal of Biomolecular NMR
#336
of 615 outputs
Outputs of similar age
#168,269
of 329,199 outputs
Outputs of similar age from Journal of Biomolecular NMR
#3
of 10 outputs
Altmetric has tracked 23,094,276 research outputs across all sources so far. This one is in the 41st percentile – i.e., 41% of other outputs scored the same or lower than it.
So far Altmetric has tracked 615 research outputs from this source. They receive a mean Attention Score of 2.9. This one is in the 44th percentile – i.e., 44% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 329,199 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 48th percentile – i.e., 48% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 10 others from the same source and published within six weeks on either side of this one. This one has scored higher than 7 of them.