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Mendeley readers
Attention Score in Context
| Title |
A novel biclustering algorithm of binary microarray data: BiBinCons and BiBinAlter
|
|---|---|
| Published in |
BioData Mining, November 2015
|
| DOI | 10.1186/s13040-015-0070-4 |
| Pubmed ID | |
| Authors |
Haifa Ben Saber, Mourad Elloumi |
| Abstract |
The biclustering of microarray data has been the subject of a large research. No one of the existing biclustering algorithms is perfect. The construction of biologically significant groups of biclusters for large microarray data is still a problem that requires a continuous work. Biological validation of biclusters of microarray data is one of the most important open issues. So far, there are no general guidelines in the literature on how to validate biologically extracted biclusters. In this paper, we develop two biclustering algorithms of binary microarray data, adopting the Iterative Row and Column Clustering Combination (IRCCC) approach, called BiBinCons and BiBinAlter. However, the BiBinAlter algorithm is an improvement of BiBinCons. On the other hand, BiBinAlter differs from BiBinCons by the use of the EvalStab and IndHomog evaluation functions in addition to the CroBin one (Bioinformatics 20:1993-2003, 2004). BiBinAlter can extracts biclusters of good quality with better p-values. |
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 % |
|---|---|---|
| Hungary | 1 | 50% |
| Unknown | 1 | 50% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 2 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 11 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 11 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 5 | 45% |
| Lecturer | 1 | 9% |
| Student > Doctoral Student | 1 | 9% |
| Student > Ph. D. Student | 1 | 9% |
| Student > Bachelor | 1 | 9% |
| Other | 2 | 18% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 4 | 36% |
| Biochemistry, Genetics and Molecular Biology | 3 | 27% |
| Agricultural and Biological Sciences | 3 | 27% |
| Neuroscience | 1 | 9% |
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 30 November 2015.
All research outputs
#15,351,145
of 22,834,308 outputs
Outputs from BioData Mining
#225
of 307 outputs
Outputs of similar age
#227,219
of 387,537 outputs
Outputs of similar age from BioData Mining
#15
of 18 outputs
Altmetric has tracked 22,834,308 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 307 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 7.7. This one is in the 19th percentile – i.e., 19% 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 387,537 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 32nd percentile – i.e., 32% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 18 others from the same source and published within six weeks on either side of this one. This one is in the 11th percentile – i.e., 11% of its contemporaries scored the same or lower than it.