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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Molecular pathway identification using biological network-regularized logistic models
|
|---|---|
| Published in |
BMC Genomics, December 2013
|
| DOI | 10.1186/1471-2164-14-s8-s7 |
| Pubmed ID | |
| Authors |
Wen Zhang, Ying-wooi Wan, Genevera I Allen, Kaifang Pang, Matthew L Anderson, Zhandong Liu |
| Abstract |
Selecting genes and pathways indicative of disease is a central problem in computational biology. This problem is especially challenging when parsing multi-dimensional genomic data. A number of tools, such as L1-norm based regularization and its extensions elastic net and fused lasso, have been introduced to deal with this challenge. However, these approaches tend to ignore the vast amount of a priori biological network information curated in the literature. |
X Demographics
The data shown below were collected from the profiles of 4 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 | 25% |
| Unknown | 3 | 75% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 4 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 53 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Portugal | 1 | 2% |
| France | 1 | 2% |
| Unknown | 51 | 96% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 14 | 26% |
| Student > Master | 8 | 15% |
| Researcher | 7 | 13% |
| Other | 5 | 9% |
| Student > Doctoral Student | 3 | 6% |
| Other | 6 | 11% |
| Unknown | 10 | 19% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 13 | 25% |
| Computer Science | 9 | 17% |
| Biochemistry, Genetics and Molecular Biology | 6 | 11% |
| Medicine and Dentistry | 4 | 8% |
| Engineering | 4 | 8% |
| Other | 7 | 13% |
| Unknown | 10 | 19% |
Attention Score in Context
This research output has an Altmetric Attention Score of 6. 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 08 October 2019.
All research outputs
#6,026,370
of 23,896,578 outputs
Outputs from BMC Genomics
#2,415
of 10,856 outputs
Outputs of similar age
#67,777
of 314,695 outputs
Outputs of similar age from BMC Genomics
#109
of 445 outputs
Altmetric has tracked 23,896,578 research outputs across all sources so far. This one has received more attention than most of these and is in the 74th percentile.
So far Altmetric has tracked 10,856 research outputs from this source. They receive a mean Attention Score of 4.8. This one has done well, scoring higher than 77% of its peers.
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 314,695 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 78% of its contemporaries.
We're also able to compare this research output to 445 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 75% of its contemporaries.