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Mendeley readers
Attention Score in Context
| Title |
BicPAM: Pattern-based biclustering for biomedical data analysis
|
|---|---|
| Published in |
Algorithms for Molecular Biology, December 2014
|
| DOI | 10.1186/s13015-014-0027-z |
| Pubmed ID | |
| Authors |
Rui Henriques, Sara C Madeira |
| Abstract |
Biclustering, the discovery of sets of objects with a coherent pattern across a subset of conditions, is a critical task to study a wide-set of biomedical problems, where molecular units or patients are meaningfully related with a set of properties. The challenging combinatorial nature of this task led to the development of approaches with restrictions on the allowed type, number and quality of biclusters. Contrasting, recent biclustering approaches relying on pattern mining methods can exhaustively discover flexible structures of robust biclusters. However, these approaches are only prepared to discover constant biclusters and their underlying contributions remain dispersed. |
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 % |
|---|---|---|
| Scientists | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 38 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Portugal | 1 | 3% |
| France | 1 | 3% |
| Unknown | 36 | 95% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Master | 10 | 26% |
| Researcher | 7 | 18% |
| Student > Ph. D. Student | 7 | 18% |
| Student > Bachelor | 4 | 11% |
| Professor | 2 | 5% |
| Other | 3 | 8% |
| Unknown | 5 | 13% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 15 | 39% |
| Biochemistry, Genetics and Molecular Biology | 5 | 13% |
| Agricultural and Biological Sciences | 4 | 11% |
| Mathematics | 3 | 8% |
| Medicine and Dentistry | 2 | 5% |
| Other | 4 | 11% |
| Unknown | 5 | 13% |
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 23 December 2014.
All research outputs
#20,247,117
of 22,775,504 outputs
Outputs from Algorithms for Molecular Biology
#233
of 264 outputs
Outputs of similar age
#297,033
of 354,383 outputs
Outputs of similar age from Algorithms for Molecular Biology
#7
of 10 outputs
Altmetric has tracked 22,775,504 research outputs across all sources so far. This one is in the 1st percentile – i.e., 1% 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 1st percentile – i.e., 1% of its peers scored the same or lower than it.
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We're also able to compare this research output to 10 others from the same source and published within six weeks on either side of this one. This one has scored higher than 3 of them.