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Mendeley readers
Attention Score in Context
| Title |
Unravelling associations between unassigned mass spectrometry peaks with frequent itemset mining techniques
|
|---|---|
| Published in |
Proteome Science, November 2014
|
| DOI | 10.1186/s12953-014-0054-1 |
| Pubmed ID | |
| Authors |
Trung Nghia Vu, Aida Mrzic, Dirk Valkenborg, Evelyne Maes, Filip Lemière, Bart Goethals, Kris Laukens |
| Abstract |
Mass spectrometry-based proteomics experiments generate spectra that are rich in information. Often only a fraction of this information is used for peptide/protein identification, whereas a significant proportion of the peaks in a spectrum remain unexplained. In this paper we explore how a specific class of data mining techniques termed "frequent itemset mining" can be employed to discover patterns in the unassigned data, and how such patterns can help us interpret the origin of the unexpected/unexplained peaks. |
X Demographics
The data shown below were collected from the profiles of 7 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 | 14% |
| Italy | 1 | 14% |
| Belgium | 1 | 14% |
| Unknown | 4 | 57% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 5 | 71% |
| Scientists | 1 | 14% |
| Science communicators (journalists, bloggers, editors) | 1 | 14% |
Mendeley readers
The data shown below were compiled from readership statistics for 24 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Belgium | 2 | 8% |
| United Kingdom | 1 | 4% |
| Brazil | 1 | 4% |
| Unknown | 20 | 83% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 9 | 38% |
| Student > Bachelor | 4 | 17% |
| Student > Ph. D. Student | 4 | 17% |
| Professor > Associate Professor | 2 | 8% |
| Student > Master | 2 | 8% |
| Other | 2 | 8% |
| Unknown | 1 | 4% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 7 | 29% |
| Computer Science | 5 | 21% |
| Mathematics | 2 | 8% |
| Biochemistry, Genetics and Molecular Biology | 2 | 8% |
| Engineering | 2 | 8% |
| Other | 5 | 21% |
| Unknown | 1 | 4% |
Attention Score in Context
This research output has an Altmetric Attention Score of 6. 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 03 September 2016.
All research outputs
#6,606,639
of 25,756,911 outputs
Outputs from Proteome Science
#32
of 210 outputs
Outputs of similar age
#82,375
of 372,028 outputs
Outputs of similar age from Proteome Science
#1
of 2 outputs
Altmetric has tracked 25,756,911 research outputs across all sources so far. This one has received more attention than most of these and is in the 74th percentile.
So far Altmetric has tracked 210 research outputs from this source. They receive a mean Attention Score of 2.8. This one has done well, scoring higher than 84% 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 372,028 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 77% of its contemporaries.
We're also able to compare this research output to 2 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them