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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Computational identification of surrogate genes for prostate cancer phases using machine learning and molecular network analysis
|
|---|---|
| Published in |
Theoretical Biology and Medical Modelling, August 2014
|
| DOI | 10.1186/1742-4682-11-37 |
| Pubmed ID | |
| Authors |
Rudong Li, Xiao Dong, Chengcheng Ma, Lei Liu |
| Abstract |
Prostate cancer is one of the most common malignant diseases and is characterized by heterogeneity in the clinical course. To date, there are no efficient morphologic features or genomic biomarkers that can characterize the phenotypes of the cancer, especially with regard to metastasis - the most adverse outcome. Searching for effective surrogate genes out of large quantities of gene expression data is a key to cancer phenotyping and/or understanding molecular mechanisms underlying prostate cancer development. |
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 % |
|---|---|---|
| Unknown | 2 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 2 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 36 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Brazil | 2 | 6% |
| Unknown | 34 | 94% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 8 | 22% |
| Student > Ph. D. Student | 8 | 22% |
| Student > Bachelor | 4 | 11% |
| Student > Doctoral Student | 3 | 8% |
| Other | 1 | 3% |
| Other | 3 | 8% |
| Unknown | 9 | 25% |
| Readers by discipline | Count | As % |
|---|---|---|
| Medicine and Dentistry | 9 | 25% |
| Computer Science | 7 | 19% |
| Agricultural and Biological Sciences | 3 | 8% |
| Biochemistry, Genetics and Molecular Biology | 1 | 3% |
| Social Sciences | 1 | 3% |
| Other | 3 | 8% |
| Unknown | 12 | 33% |
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 26 August 2014.
All research outputs
#18,376,927
of 22,761,738 outputs
Outputs from Theoretical Biology and Medical Modelling
#215
of 287 outputs
Outputs of similar age
#168,021
of 235,668 outputs
Outputs of similar age from Theoretical Biology and Medical Modelling
#5
of 11 outputs
Altmetric has tracked 22,761,738 research outputs across all sources so far. This one is in the 11th percentile – i.e., 11% of other outputs scored the same or lower than it.
So far Altmetric has tracked 287 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 7.4. This one is in the 14th percentile – i.e., 14% 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 235,668 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 16th percentile – i.e., 16% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 11 others from the same source and published within six weeks on either side of this one. This one is in the 18th percentile – i.e., 18% of its contemporaries scored the same or lower than it.