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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Normalization and missing value imputation for label-free LC-MS analysis
|
|---|---|
| Published in |
BMC Bioinformatics, November 2012
|
| DOI | 10.1186/1471-2105-13-s16-s5 |
| Pubmed ID | |
| Authors |
Yuliya V Karpievitch, Alan R Dabney, Richard D Smith |
| Abstract |
Shotgun proteomic data are affected by a variety of known and unknown systematic biases as well as high proportions of missing values. Typically, normalization is performed in an attempt to remove systematic biases from the data before statistical inference, sometimes followed by missing value imputation to obtain a complete matrix of intensities. Here we discuss several approaches to normalization and dealing with missing values, some initially developed for microarray data and some developed specifically for mass spectrometry-based data. |
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% |
| United States | 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 464 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United Kingdom | 4 | <1% |
| France | 2 | <1% |
| United States | 2 | <1% |
| Australia | 1 | <1% |
| Sweden | 1 | <1% |
| Korea, Republic of | 1 | <1% |
| India | 1 | <1% |
| Germany | 1 | <1% |
| Spain | 1 | <1% |
| Other | 1 | <1% |
| Unknown | 449 | 97% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 137 | 30% |
| Researcher | 96 | 21% |
| Student > Master | 60 | 13% |
| Student > Bachelor | 29 | 6% |
| Student > Doctoral Student | 24 | 5% |
| Other | 57 | 12% |
| Unknown | 61 | 13% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 132 | 28% |
| Biochemistry, Genetics and Molecular Biology | 104 | 22% |
| Computer Science | 26 | 6% |
| Chemistry | 24 | 5% |
| Medicine and Dentistry | 19 | 4% |
| Other | 83 | 18% |
| Unknown | 76 | 16% |
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 01 April 2014.
All research outputs
#15,273,442
of 22,712,476 outputs
Outputs from BMC Bioinformatics
#5,364
of 7,259 outputs
Outputs of similar age
#115,554
of 183,430 outputs
Outputs of similar age from BMC Bioinformatics
#74
of 112 outputs
Altmetric has tracked 22,712,476 research outputs across all sources so far. This one is in the 22nd percentile – i.e., 22% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,259 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. This one is in the 18th percentile – i.e., 18% 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 183,430 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 27th percentile – i.e., 27% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 112 others from the same source and published within six weeks on either side of this one. This one is in the 25th percentile – i.e., 25% of its contemporaries scored the same or lower than it.