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Mendeley readers
Attention Score in Context
| Title |
DeBi: Discovering Differentially Expressed Biclusters using a Frequent Itemset Approach
|
|---|---|
| Published in |
Algorithms for Molecular Biology, June 2011
|
| DOI | 10.1186/1748-7188-6-18 |
| Pubmed ID | |
| Authors |
Akdes Serin, Martin Vingron |
| Abstract |
The analysis of massive high throughput data via clustering algorithms is very important for elucidating gene functions in biological systems. However, traditional clustering methods have several drawbacks. Biclustering overcomes these limitations by grouping genes and samples simultaneously. It discovers subsets of genes that are co-expressed in certain samples. Recent studies showed that biclustering has a great potential in detecting marker genes that are associated with certain tissues or diseases. Several biclustering algorithms have been proposed. However, it is still a challenge to find biclusters that are significant based on biological validation measures. Besides that, there is a need for a biclustering algorithm that is capable of analyzing very large datasets in reasonable time. |
X Demographics
The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 42 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Germany | 2 | 5% |
| United States | 2 | 5% |
| Finland | 1 | 2% |
| Unknown | 37 | 88% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 12 | 29% |
| Student > Ph. D. Student | 11 | 26% |
| Student > Master | 5 | 12% |
| Professor | 4 | 10% |
| Student > Bachelor | 2 | 5% |
| Other | 4 | 10% |
| Unknown | 4 | 10% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 17 | 40% |
| Agricultural and Biological Sciences | 10 | 24% |
| Biochemistry, Genetics and Molecular Biology | 5 | 12% |
| Engineering | 2 | 5% |
| Business, Management and Accounting | 1 | 2% |
| Other | 3 | 7% |
| Unknown | 4 | 10% |
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 25 September 2013.
All research outputs
#18,348,542
of 22,723,682 outputs
Outputs from Algorithms for Molecular Biology
#197
of 264 outputs
Outputs of similar age
#96,866
of 115,166 outputs
Outputs of similar age from Algorithms for Molecular Biology
#1
of 4 outputs
Altmetric has tracked 22,723,682 research outputs across all sources so far. This one is in the 11th percentile – i.e., 11% of other outputs scored the same or lower than it.
So far Altmetric has tracked 264 research outputs from this source. They receive a mean Attention Score of 3.2. This one is in the 12th percentile – i.e., 12% 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 115,166 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 7th percentile – i.e., 7% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 4 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them