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X Demographics
Mendeley readers
Attention Score in Context
| Title |
The LO-BaFL method and ALS microarray expression analysis
|
|---|---|
| Published in |
BMC Bioinformatics, September 2012
|
| DOI | 10.1186/1471-2105-13-244 |
| Pubmed ID | |
| Authors |
Cristina Baciu, Kevin J Thompson, Jean-Luc Mougeot, Benjamin R Brooks, Jennifer W Weller |
| Abstract |
Sporadic Amyotrophic Lateral Sclerosis (sALS) is a devastating, complex disease of unknown etiology. We studied this disease with microarray technology to capture as much biological complexity as possible. The Affymetrix-focused BaFL pipeline takes into account problems with probes that arise from physical and biological properties, so we adapted it to handle the long-oligonucleotide probes on our arrays (hence LO-BaFL). The revised method was tested against a validated array experiment and then used in a meta-analysis of peripheral white blood cells from healthy control samples in two experiments. We predicted differentially expressed (DE) genes in our sALS data, combining the results obtained using the TM4 suite of tools with those from the LO-BaFL method. Those predictions were tested using qRT-PCR assays. |
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 % |
|---|---|---|
| France | 1 | 50% |
| Unknown | 1 | 50% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 1 | 50% |
| Scientists | 1 | 50% |
Mendeley readers
The data shown below were compiled from readership statistics for 22 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 22 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Bachelor | 7 | 32% |
| Researcher | 6 | 27% |
| Student > Ph. D. Student | 3 | 14% |
| Student > Postgraduate | 2 | 9% |
| Student > Master | 1 | 5% |
| Other | 2 | 9% |
| Unknown | 1 | 5% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 7 | 32% |
| Biochemistry, Genetics and Molecular Biology | 6 | 27% |
| Computer Science | 2 | 9% |
| Engineering | 2 | 9% |
| Social Sciences | 1 | 5% |
| Other | 2 | 9% |
| Unknown | 2 | 9% |
Attention Score in Context
This research output has an Altmetric Attention Score of 9. 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 24 September 2012.
All research outputs
#3,761,472
of 22,679,690 outputs
Outputs from BMC Bioinformatics
#1,456
of 7,251 outputs
Outputs of similar age
#27,170
of 171,470 outputs
Outputs of similar age from BMC Bioinformatics
#16
of 105 outputs
Altmetric has tracked 22,679,690 research outputs across all sources so far. Compared to these this one has done well and is in the 83rd percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 7,251 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. This one has done well, scoring higher than 79% 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 171,470 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 84% of its contemporaries.
We're also able to compare this research output to 105 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 84% of its contemporaries.