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Mendeley readers
Attention Score in Context
| Title |
Prediction of essential proteins based on gene expression programming
|
|---|---|
| Published in |
BMC Genomics, October 2013
|
| DOI | 10.1186/1471-2164-14-s4-s7 |
| Pubmed ID | |
| Authors |
Jiancheng Zhong, Jianxin Wang, Wei Peng, Zhen Zhang, Yi Pan |
| Abstract |
Essential proteins are indispensable for cell survive. Identifying essential proteins is very important for improving our understanding the way of a cell working. There are various types of features related to the essentiality of proteins. Many methods have been proposed to combine some of them to predict essential proteins. However, it is still a big challenge for designing an effective method to predict them by integrating different features, and explaining how these selected features decide the essentiality of protein. Gene expression programming (GEP) is a learning algorithm and what it learns specifically is about relationships between variables in sets of data and then builds models to explain these relationships. |
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 41 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 1 | 2% |
| Unknown | 40 | 98% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 11 | 27% |
| Student > Master | 10 | 24% |
| Researcher | 5 | 12% |
| Student > Bachelor | 4 | 10% |
| Lecturer | 1 | 2% |
| Other | 2 | 5% |
| Unknown | 8 | 20% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 13 | 32% |
| Agricultural and Biological Sciences | 8 | 20% |
| Biochemistry, Genetics and Molecular Biology | 6 | 15% |
| Engineering | 4 | 10% |
| Medicine and Dentistry | 1 | 2% |
| Other | 1 | 2% |
| Unknown | 8 | 20% |
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 10 July 2014.
All research outputs
#20,232,430
of 22,758,248 outputs
Outputs from BMC Genomics
#9,263
of 10,637 outputs
Outputs of similar age
#181,240
of 207,149 outputs
Outputs of similar age from BMC Genomics
#114
of 148 outputs
Altmetric has tracked 22,758,248 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 10,637 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 207,149 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 148 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.