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Attention Score in Context
| Title |
Combination of meta-analysis and graph clustering to identify prognostic markers of ESCC
|
|---|---|
| Published in |
Genetics and Molecular Biology, June 2012
|
| DOI | 10.1590/s1415-47572012000300021 |
| Pubmed ID | |
| Authors |
Hongyun Gao, Lishan Wang, Shitao Cui, Mingsong Wang |
| Abstract |
Esophageal squamous cell carcinoma (ESCC) is one of the most malignant gastrointestinal cancers and occurs at a high frequency rate in China and other Asian countries. Recently, several molecular markers were identified for predicting ESCC. Notwithstanding, additional prognostic markers, with a clear understanding of their underlying roles, are still required. Through bioinformatics, a graph-clustering method by DPClus was used to detect co-expressed modules. The aim was to identify a set of discriminating genes that could be used for predicting ESCC through graph-clustering and GO-term analysis. The results showed that CXCL12, CYP2C9, TGM3, MAL, S100A9, EMP-1 and SPRR3 were highly associated with ESCC development. In our study, all their predicted roles were in line with previous reports, whereby the assumption that a combination of meta-analysis, graph-clustering and GO-term analysis is effective for both identifying differentially expressed genes, and reflecting on their functions in ESCC. |
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 % |
|---|---|---|
| United States | 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 15 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 15 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Bachelor | 3 | 20% |
| Researcher | 2 | 13% |
| Other | 2 | 13% |
| Student > Doctoral Student | 1 | 7% |
| Lecturer | 1 | 7% |
| Other | 5 | 33% |
| Unknown | 1 | 7% |
| Readers by discipline | Count | As % |
|---|---|---|
| Medicine and Dentistry | 4 | 27% |
| Biochemistry, Genetics and Molecular Biology | 3 | 20% |
| Computer Science | 3 | 20% |
| Engineering | 2 | 13% |
| Social Sciences | 1 | 7% |
| Other | 1 | 7% |
| Unknown | 1 | 7% |
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 23 June 2012.
All research outputs
#20,655,488
of 25,371,288 outputs
Outputs from Genetics and Molecular Biology
#551
of 771 outputs
Outputs of similar age
#138,715
of 177,625 outputs
Outputs of similar age from Genetics and Molecular Biology
#12
of 22 outputs
Altmetric has tracked 25,371,288 research outputs across all sources so far. This one is in the 10th percentile – i.e., 10% of other outputs scored the same or lower than it.
So far Altmetric has tracked 771 research outputs from this source. They receive a mean Attention Score of 4.8. This one is in the 16th percentile – i.e., 16% of its peers scored the same or lower than it.
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We're also able to compare this research output to 22 others from the same source and published within six weeks on either side of this one. This one is in the 13th percentile – i.e., 13% of its contemporaries scored the same or lower than it.