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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Integrating multiple networks for protein function prediction
|
|---|---|
| Published in |
BMC Systems Biology, January 2015
|
| DOI | 10.1186/1752-0509-9-s1-s3 |
| Pubmed ID | |
| Authors |
Guoxian Yu, Hailong Zhu, Carlotta Domeniconi, Maozu Guo |
| Abstract |
High throughput techniques produce multiple functional association networks. Integrating these networks can enhance the accuracy of protein function prediction. Many algorithms have been introduced to generate a composite network, which is obtained as a weighted sum of individual networks. The weight assigned to an individual network reflects its benefit towards the protein functional annotation inference. A classifier is then trained on the composite network for predicting protein functions. However, since these techniques model the optimization of the composite network and the prediction tasks as separate objectives, the resulting composite network is not necessarily optimal for the follow-up protein function prediction. |
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 % |
|---|---|---|
| France | 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 30 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 30 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Master | 7 | 23% |
| Student > Bachelor | 4 | 13% |
| Researcher | 4 | 13% |
| Student > Ph. D. Student | 3 | 10% |
| Professor > Associate Professor | 2 | 7% |
| Other | 3 | 10% |
| Unknown | 7 | 23% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 9 | 30% |
| Biochemistry, Genetics and Molecular Biology | 8 | 27% |
| Agricultural and Biological Sciences | 4 | 13% |
| Unspecified | 1 | 3% |
| Sports and Recreations | 1 | 3% |
| Other | 0 | 0% |
| Unknown | 7 | 23% |
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 February 2015.
All research outputs
#22,759,452
of 25,374,647 outputs
Outputs from BMC Systems Biology
#1,004
of 1,132 outputs
Outputs of similar age
#307,627
of 359,555 outputs
Outputs of similar age from BMC Systems Biology
#32
of 42 outputs
Altmetric has tracked 25,374,647 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 1,132 research outputs from this source. They receive a mean Attention Score of 3.7. 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 359,555 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 42 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.