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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Learning the structure of gene regulatory networks from time series gene expression data
|
|---|---|
| Published in |
BMC Genomics, December 2011
|
| DOI | 10.1186/1471-2164-12-s5-s13 |
| Pubmed ID | |
| Authors |
Haoni Li, Nan Wang, Ping Gong, Edward J Perkins, Chaoyang Zhang |
| Abstract |
Dynamic Bayesian Network (DBN) is an approach widely used for reconstruction of gene regulatory networks from time-series microarray data. Its performance in network reconstruction depends on a structure learning algorithm. REVEAL (REVerse Engineering ALgorithm) is one of the algorithms implemented for learning DBN structure and used to reconstruct gene regulatory networks (GRN). However, the two-stage temporal Bayes network (2TBN) structure of DBN that specifies correlation between time slices cannot be obtained by score metrics used in REVEAL. |
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 49 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Brazil | 2 | 4% |
| Germany | 1 | 2% |
| Australia | 1 | 2% |
| Switzerland | 1 | 2% |
| Canada | 1 | 2% |
| United States | 1 | 2% |
| Unknown | 42 | 86% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 16 | 33% |
| Student > Ph. D. Student | 11 | 22% |
| Student > Master | 6 | 12% |
| Student > Bachelor | 4 | 8% |
| Professor | 3 | 6% |
| Other | 7 | 14% |
| Unknown | 2 | 4% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 20 | 41% |
| Computer Science | 9 | 18% |
| Engineering | 5 | 10% |
| Biochemistry, Genetics and Molecular Biology | 4 | 8% |
| Pharmacology, Toxicology and Pharmaceutical Science | 2 | 4% |
| Other | 5 | 10% |
| Unknown | 4 | 8% |
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 December 2011.
All research outputs
#20,153,534
of 22,660,862 outputs
Outputs from BMC Genomics
#9,240
of 10,612 outputs
Outputs of similar age
#220,435
of 243,183 outputs
Outputs of similar age from BMC Genomics
#271
of 298 outputs
Altmetric has tracked 22,660,862 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 10,612 research outputs from this source. They receive a mean Attention Score of 4.7. This one is in the 1st percentile – i.e., 1% 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 243,183 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 298 others from the same source and published within six weeks on either side of this one. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.