| Title |
Cloud-based solution to identify statistically significant MS peaks differentiating sample categories
|
|---|---|
| Published in |
BMC Research Notes, March 2013
|
| DOI | 10.1186/1756-0500-6-109 |
| Pubmed ID | |
| Authors |
Jun Ji, Jeffrey Ling, Helen Jiang, Qiaojun Wen, John C Whitin, Lu Tian, Harvey J Cohen, Xuefeng B Ling |
| Abstract |
Mass spectrometry (MS) has evolved to become the primary high throughput tool for proteomics based biomarker discovery. Until now, multiple challenges in protein MS data analysis remain: large-scale and complex data set management; MS peak identification, indexing; and high dimensional peak differential analysis with the concurrent statistical tests based false discovery rate (FDR). "Turnkey" solutions are needed for biomarker investigations to rapidly process MS data sets to identify statistically significant peaks for subsequent validation. |
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 % |
|---|---|---|
| India | 1 | 50% |
| United Kingdom | 1 | 50% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 2 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 7 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| India | 1 | 14% |
| Unknown | 6 | 86% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 2 | 29% |
| Student > Bachelor | 2 | 29% |
| Researcher | 1 | 14% |
| Student > Master | 1 | 14% |
| Unknown | 1 | 14% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 4 | 57% |
| Computer Science | 2 | 29% |
| Unknown | 1 | 14% |
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 27 March 2013.
All research outputs
#15,266,089
of 22,701,287 outputs
Outputs from BMC Research Notes
#2,312
of 4,255 outputs
Outputs of similar age
#123,696
of 197,511 outputs
Outputs of similar age from BMC Research Notes
#36
of 57 outputs
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So far Altmetric has tracked 4,255 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.5. This one is in the 33rd percentile – i.e., 33% of its peers scored the same or lower than it.
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We're also able to compare this research output to 57 others from the same source and published within six weeks on either side of this one. This one is in the 24th percentile – i.e., 24% of its contemporaries scored the same or lower than it.