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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Cardiovascular proteomics in the era of big data: experimental and computational advances
|
|---|---|
| Published in |
Clinical Proteomics, December 2016
|
| DOI | 10.1186/s12014-016-9124-y |
| Pubmed ID | |
| Authors |
Maggie P. Y. Lam, Edward Lau, Dominic C. M. Ng, Ding Wang, Peipei Ping |
| Abstract |
Proteomics plays an increasingly important role in our quest to understand cardiovascular biology. Fueled by analytical and computational advances in the past decade, proteomics applications can now go beyond merely inventorying protein species, and address sophisticated questions on cardiac physiology. The advent of massive mass spectrometry datasets has in turn led to increasing intersection between proteomics and big data science. Here we review new frontiers in technological developments and their applications to cardiovascular medicine. The impact of big data science on cardiovascular proteomics investigations and translation to medicine is highlighted. |
X Demographics
The data shown below were collected from the profiles of 5 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 States | 2 | 40% |
| Unknown | 3 | 60% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 4 | 80% |
| Scientists | 1 | 20% |
Mendeley readers
The data shown below were compiled from readership statistics for 63 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 63 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 15 | 24% |
| Student > Ph. D. Student | 11 | 17% |
| Student > Bachelor | 9 | 14% |
| Student > Master | 4 | 6% |
| Student > Doctoral Student | 4 | 6% |
| Other | 11 | 17% |
| Unknown | 9 | 14% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 12 | 19% |
| Biochemistry, Genetics and Molecular Biology | 11 | 17% |
| Computer Science | 7 | 11% |
| Medicine and Dentistry | 6 | 10% |
| Engineering | 4 | 6% |
| Other | 11 | 17% |
| Unknown | 12 | 19% |
Attention Score in Context
This research output has an Altmetric Attention Score of 13. 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 02 October 2019.
All research outputs
#2,372,610
of 22,908,162 outputs
Outputs from Clinical Proteomics
#19
of 285 outputs
Outputs of similar age
#49,589
of 415,991 outputs
Outputs of similar age from Clinical Proteomics
#2
of 12 outputs
Altmetric has tracked 22,908,162 research outputs across all sources so far. Compared to these this one has done well and is in the 89th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 285 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.2. This one has done particularly well, scoring higher than 93% 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 415,991 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 88% of its contemporaries.
We're also able to compare this research output to 12 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 83% of its contemporaries.