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Mendeley readers
Attention Score in Context
| Title |
A group LASSO-based method for robustly inferring gene regulatory networks from multiple time-course datasets
|
|---|---|
| Published in |
BMC Systems Biology, October 2014
|
| DOI | 10.1186/1752-0509-8-s3-s1 |
| Pubmed ID | |
| Authors |
Li-Zhi Liu, Fang-Xiang Wu, Wen-Jun Zhang |
| Abstract |
As an abstract mapping of the gene regulations in the cell, gene regulatory network is important to both biological research study and practical applications. The reverse engineering of gene regulatory networks from microarray gene expression data is a challenging research problem in systems biology. With the development of biological technologies, multiple time-course gene expression datasets might be collected for a specific gene network under different circumstances. The inference of a gene regulatory network can be improved by integrating these multiple datasets. It is also known that gene expression data may be contaminated with large errors or outliers, which may affect the inference results. |
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 % |
|---|---|---|
| France | 1 | 50% |
| United States | 1 | 50% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 2 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 31 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Netherlands | 1 | 3% |
| Germany | 1 | 3% |
| Unknown | 29 | 94% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 12 | 39% |
| Student > Ph. D. Student | 9 | 29% |
| Professor | 2 | 6% |
| Student > Master | 2 | 6% |
| Lecturer | 1 | 3% |
| Other | 2 | 6% |
| Unknown | 3 | 10% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 7 | 23% |
| Biochemistry, Genetics and Molecular Biology | 6 | 19% |
| Computer Science | 6 | 19% |
| Mathematics | 2 | 6% |
| Social Sciences | 2 | 6% |
| Other | 4 | 13% |
| Unknown | 4 | 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 25 June 2015.
All research outputs
#17,730,142
of 22,768,097 outputs
Outputs from BMC Systems Biology
#770
of 1,142 outputs
Outputs of similar age
#175,306
of 260,342 outputs
Outputs of similar age from BMC Systems Biology
#23
of 31 outputs
Altmetric has tracked 22,768,097 research outputs across all sources so far. This one is in the 19th percentile – i.e., 19% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,142 research outputs from this source. They receive a mean Attention Score of 3.6. This one is in the 27th percentile – i.e., 27% 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 260,342 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 28th percentile – i.e., 28% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 31 others from the same source and published within six weeks on either side of this one. This one is in the 19th percentile – i.e., 19% of its contemporaries scored the same or lower than it.