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X Demographics
Mendeley readers
Attention Score in Context
| Chapter title |
CATH-Gene3D: Generation of the Resource and Its Use in Obtaining Structural and Functional Annotations for Protein Sequences
|
|---|---|
| Chapter number | 4 |
| Book title |
Protein Bioinformatics
|
| Published in |
Methods in molecular biology, February 2017
|
| DOI | 10.1007/978-1-4939-6783-4_4 |
| Pubmed ID | |
| Book ISBNs |
978-1-4939-6781-0, 978-1-4939-6783-4
|
| Authors |
Natalie L. Dawson, Ian Sillitoe, Jonathan G. Lees, Su Datt Lam, Christine A. Orengo |
| Editors |
Cathy H. Wu, Cecilia N. Arighi, Karen E. Ross |
| Abstract |
This chapter describes the generation of the data in the CATH-Gene3D online resource and how it can be used to study protein domains and their evolutionary relationships. Methods will be presented for: comparing protein structures, recognizing homologs, predicting domain structures within protein sequences, and subclassifying superfamilies into functionally pure families, together with a guide on using the webpages. |
X Demographics
The data shown below were collected from the profiles of 2 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United Kingdom | 1 | 50% |
| Unknown | 1 | 50% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 1 | 50% |
| Members of the public | 1 | 50% |
Mendeley readers
The data shown below were compiled from readership statistics for 29 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 29 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 6 | 21% |
| Student > Bachelor | 6 | 21% |
| Student > Master | 5 | 17% |
| Researcher | 2 | 7% |
| Other | 1 | 3% |
| Other | 4 | 14% |
| Unknown | 5 | 17% |
| Readers by discipline | Count | As % |
|---|---|---|
| Biochemistry, Genetics and Molecular Biology | 8 | 28% |
| Agricultural and Biological Sciences | 7 | 24% |
| Computer Science | 6 | 21% |
| Unspecified | 1 | 3% |
| Immunology and Microbiology | 1 | 3% |
| Other | 0 | 0% |
| Unknown | 6 | 21% |
Attention Score in Context
This research output has an Altmetric Attention Score of 2. 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 01 March 2017.
All research outputs
#15,268,318
of 24,226,848 outputs
Outputs from Methods in molecular biology
#4,487
of 13,632 outputs
Outputs of similar age
#237,587
of 427,915 outputs
Outputs of similar age from Methods in molecular biology
#389
of 1,175 outputs
Altmetric has tracked 24,226,848 research outputs across all sources so far. This one is in the 34th percentile – i.e., 34% of other outputs scored the same or lower than it.
So far Altmetric has tracked 13,632 research outputs from this source. They receive a mean Attention Score of 3.5. This one has gotten more attention than average, scoring higher than 63% of its peers.
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 427,915 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 41st percentile – i.e., 41% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 1,175 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 63% of its contemporaries.