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Attention Score in Context
Chapter title |
Hidden Markov Models for Protein Domain Homology Identification and Analysis
|
---|---|
Chapter number | 3 |
Book title |
SH2 Domains
|
Published in |
Methods in molecular biology, January 2017
|
DOI | 10.1007/978-1-4939-6762-9_3 |
Pubmed ID | |
Book ISBNs |
978-1-4939-6760-5, 978-1-4939-6762-9
|
Authors |
Karl Jablonowski |
Editors |
Kazuya Machida, Bernard A. Liu |
Abstract |
Protein domain identification and analysis are cornerstones of modern proteomics. The tools available to protein domain researchers avail a variety of approaches to understanding large protein domain families. Hidden Markov Models (HMM) form the basis for identifying and categorizing evolutionarily linked protein domains. Here I describe the use of HMM models for predicting and identifying Src Homology 2 (SH2) domains within the proteome. |
X Demographics
The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
Country | Count | As % |
---|---|---|
Unknown | 1 | 100% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Members of the public | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 8 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
Country | Count | As % |
---|---|---|
Unknown | 8 | 100% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Researcher | 2 | 25% |
Student > Doctoral Student | 1 | 13% |
Student > Ph. D. Student | 1 | 13% |
Other | 1 | 13% |
Unknown | 3 | 38% |
Readers by discipline | Count | As % |
---|---|---|
Biochemistry, Genetics and Molecular Biology | 2 | 25% |
Agricultural and Biological Sciences | 1 | 13% |
Medicine and Dentistry | 1 | 13% |
Chemistry | 1 | 13% |
Unknown | 3 | 38% |
Attention Score in Context
This research output has an Altmetric Attention Score of 1. 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 17 January 2017.
All research outputs
#20,390,619
of 22,940,083 outputs
Outputs from Methods in molecular biology
#9,915
of 13,127 outputs
Outputs of similar age
#355,763
of 420,863 outputs
Outputs of similar age from Methods in molecular biology
#842
of 1,074 outputs
Altmetric has tracked 22,940,083 research outputs across all sources so far. This one is in the 1st percentile – i.e., 1% of other outputs scored the same or lower than it.
So far Altmetric has tracked 13,127 research outputs from this source. They receive a mean Attention Score of 3.4. This one is in the 1st percentile – i.e., 1% 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 420,863 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 1,074 others from the same source and published within six weeks on either side of this one. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.