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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Reordering based integrative expression profiling for microarray classification
|
|---|---|
| Published in |
BMC Bioinformatics, March 2012
|
| DOI | 10.1186/1471-2105-13-s2-s1 |
| Pubmed ID | |
| Authors |
Xiaogang Wu, Hui Huang, Madhankumar Sonachalam, Sina Reinhard, Jeffrey Shen, Ragini Pandey, Jake Y Chen |
| Abstract |
Current network-based microarray analysis uses the information of interactions among concerned genes/gene products, but still considers each gene expression individually. We propose an organized knowledge-supervised approach - Integrative eXpression Profiling (IXP), to improve microarray classification accuracy, and help discover groups of genes that have been too weak to detect individually by traditional ways. To implement IXP, ant colony optimization reordering (ACOR) algorithm is used to group functionally related genes in an ordered way. |
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 States | 1 | 50% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 2 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 17 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 1 | 6% |
| Unknown | 16 | 94% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 6 | 35% |
| Student > Ph. D. Student | 3 | 18% |
| Student > Bachelor | 2 | 12% |
| Professor | 2 | 12% |
| Student > Master | 1 | 6% |
| Other | 2 | 12% |
| Unknown | 1 | 6% |
| Readers by discipline | Count | As % |
|---|---|---|
| Psychology | 3 | 18% |
| Medicine and Dentistry | 3 | 18% |
| Computer Science | 3 | 18% |
| Neuroscience | 2 | 12% |
| Linguistics | 1 | 6% |
| Other | 3 | 18% |
| Unknown | 2 | 12% |
Attention Score in Context
This research output has an Altmetric Attention Score of 2. 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 21 January 2013.
All research outputs
#14,598,470
of 22,663,969 outputs
Outputs from BMC Bioinformatics
#5,009
of 7,247 outputs
Outputs of similar age
#96,426
of 156,636 outputs
Outputs of similar age from BMC Bioinformatics
#45
of 62 outputs
Altmetric has tracked 22,663,969 research outputs across all sources so far. This one is in the 35th percentile – i.e., 35% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,247 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 30th percentile – i.e., 30% 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 156,636 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 37th percentile – i.e., 37% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 62 others from the same source and published within six weeks on either side of this one. This one is in the 27th percentile – i.e., 27% of its contemporaries scored the same or lower than it.