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Mendeley readers
Attention Score in Context
| Title |
Mining the tissue-tissue gene co-expression network for tumor microenvironment study and biomarker prediction
|
|---|---|
| Published in |
BMC Genomics, October 2013
|
| DOI | 10.1186/1471-2164-14-s5-s4 |
| Pubmed ID | |
| Authors |
Yang Xiang, Jie Zhang, Kun Huang |
| Abstract |
Recent discovery in tumor development indicates that the tumor microenvironment (mostly stroma cells) plays an important role in cancer development. To understand how the tumor microenvironment (TME) interacts with the tumor, we explore the correlation of the gene expressions between tumor and stroma. The tumor and stroma gene expression data are modeled as a weighted bipartite network (tumor-stroma coexpression network) where the weight of an edge indicates the correlation between the expression profiles of the corresponding tumor gene and stroma gene. In order to efficiently mine this weighted bipartite network, we developed the Bipartite subnetwork Component Mining algorithm (BCM), and we show that the BCM algorithm can efficiently mine weighted bipartite networks for dense Bipartite sub-Networks (BiNets) with density guarantees. |
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 42 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Hungary | 1 | 2% |
| Denmark | 1 | 2% |
| Unknown | 40 | 95% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 9 | 21% |
| Student > Ph. D. Student | 8 | 19% |
| Student > Bachelor | 4 | 10% |
| Student > Master | 4 | 10% |
| Student > Doctoral Student | 3 | 7% |
| Other | 8 | 19% |
| Unknown | 6 | 14% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 11 | 26% |
| Agricultural and Biological Sciences | 8 | 19% |
| Biochemistry, Genetics and Molecular Biology | 6 | 14% |
| Medicine and Dentistry | 6 | 14% |
| Engineering | 2 | 5% |
| Other | 3 | 7% |
| Unknown | 6 | 14% |
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 31 October 2014.
All research outputs
#20,655,488
of 25,373,627 outputs
Outputs from BMC Genomics
#8,709
of 11,244 outputs
Outputs of similar age
#168,837
of 223,620 outputs
Outputs of similar age from BMC Genomics
#147
of 211 outputs
Altmetric has tracked 25,373,627 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 11,244 research outputs from this source. They receive a mean Attention Score of 4.8. This one is in the 12th percentile – i.e., 12% 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 223,620 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 11th percentile – i.e., 11% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 211 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.