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Mendeley readers
Attention Score in Context
| Title |
Learning a Markov Logic network for supervised gene regulatory network inference
|
|---|---|
| Published in |
BMC Bioinformatics, September 2013
|
| DOI | 10.1186/1471-2105-14-273 |
| Pubmed ID | |
| Authors |
Céline Brouard, Christel Vrain, Julie Dubois, David Castel, Marie-Anne Debily, Florence d’Alché-Buc |
| Abstract |
Gene regulatory network inference remains a challenging problem in systems biology despite the numerous approaches that have been proposed. When substantial knowledge on a gene regulatory network is already available, supervised network inference is appropriate. Such a method builds a binary classifier able to assign a class (Regulation/No regulation) to an ordered pair of genes. Once learnt, the pairwise classifier can be used to predict new regulations. In this work, we explore the framework of Markov Logic Networks (MLN) that combine features of probabilistic graphical models with the expressivity of first-order logic rules. |
X Demographics
The data shown below were collected from the profiles of 3 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 1 | 33% |
| Canada | 1 | 33% |
| Unknown | 1 | 33% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 2 | 67% |
| Scientists | 1 | 33% |
Mendeley readers
The data shown below were compiled from readership statistics for 54 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| France | 1 | 2% |
| Luxembourg | 1 | 2% |
| Brazil | 1 | 2% |
| Unknown | 51 | 94% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 13 | 24% |
| Student > Ph. D. Student | 9 | 17% |
| Student > Master | 6 | 11% |
| Professor > Associate Professor | 5 | 9% |
| Student > Bachelor | 4 | 7% |
| Other | 8 | 15% |
| Unknown | 9 | 17% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 14 | 26% |
| Computer Science | 12 | 22% |
| Biochemistry, Genetics and Molecular Biology | 6 | 11% |
| Engineering | 4 | 7% |
| Medicine and Dentistry | 2 | 4% |
| Other | 7 | 13% |
| Unknown | 9 | 17% |
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 14 July 2015.
All research outputs
#14,924,593
of 23,870,803 outputs
Outputs from BMC Bioinformatics
#4,826
of 7,454 outputs
Outputs of similar age
#113,708
of 201,248 outputs
Outputs of similar age from BMC Bioinformatics
#57
of 99 outputs
Altmetric has tracked 23,870,803 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 7,454 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.5. This one is in the 31st percentile – i.e., 31% 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 201,248 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 41st percentile – i.e., 41% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 99 others from the same source and published within six weeks on either side of this one. This one is in the 38th percentile – i.e., 38% of its contemporaries scored the same or lower than it.