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Mendeley readers
Attention Score in Context
| Title |
Investigating the utility of clinical outcome-guided mutual information network in network-based Cox regression
|
|---|---|
| Published in |
BMC Systems Biology, January 2015
|
| DOI | 10.1186/1752-0509-9-s1-s8 |
| Pubmed ID | |
| Authors |
Hyun-hwan Jeong, So Yeon Kim, Kyubum Wee, Kyung-Ah Sohn |
| Abstract |
Network-based approaches have recently gained considerable popularity in high- dimensional regression settings. For example, the Cox regression model is widely used in expression analysis to predict the survival of patients. However, as the number of genes becomes substantially larger than the number of samples, the traditional Cox or L2-regularized Cox models are still prone to noise and produce unreliable estimations of regression coefficients. A recent approach called the network-based Cox (Net-Cox) model attempts to resolve this issue by incorporating prior gene network information into the Cox regression. The Net-Cox model has shown to outperform the models that do not use this network information. |
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 | 2 | 50% |
| Saudi Arabia | 1 | 25% |
| Unknown | 1 | 25% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 3 | 75% |
| Members of the public | 1 | 25% |
Mendeley readers
The data shown below were compiled from readership statistics for 14 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Korea, Republic of | 1 | 7% |
| Unknown | 13 | 93% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 4 | 29% |
| Researcher | 3 | 21% |
| Other | 1 | 7% |
| Student > Doctoral Student | 1 | 7% |
| Student > Bachelor | 1 | 7% |
| Other | 3 | 21% |
| Unknown | 1 | 7% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 3 | 21% |
| Biochemistry, Genetics and Molecular Biology | 2 | 14% |
| Mathematics | 2 | 14% |
| Psychology | 1 | 7% |
| Earth and Planetary Sciences | 1 | 7% |
| Other | 4 | 29% |
| Unknown | 1 | 7% |
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 08 July 2020.
All research outputs
#13,936,629
of 22,793,427 outputs
Outputs from BMC Systems Biology
#518
of 1,142 outputs
Outputs of similar age
#181,517
of 351,785 outputs
Outputs of similar age from BMC Systems Biology
#15
of 48 outputs
Altmetric has tracked 22,793,427 research outputs across all sources so far. This one is in the 37th percentile – i.e., 37% 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 has gotten more attention than average, scoring higher than 52% 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 351,785 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 47th percentile – i.e., 47% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 48 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 66% of its contemporaries.