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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Identifying dysregulated pathways in cancers from pathway interaction networks
|
|---|---|
| Published in |
BMC Bioinformatics, June 2012
|
| DOI | 10.1186/1471-2105-13-126 |
| Pubmed ID | |
| Authors |
Ke-Qin Liu, Zhi-Ping Liu, Jin-Kao Hao, Luonan Chen, Xing-Ming Zhao |
| Abstract |
Cancers, a group of multifactorial complex diseases, are generally caused by mutation of multiple genes or dysregulation of pathways. Identifying biomarkers that can characterize cancers would help to understand and diagnose cancers. Traditional computational methods that detect genes differentially expressed between cancer and normal samples fail to work due to small sample size and independent assumption among genes. On the other hand, genes work in concert to perform their functions. Therefore, it is expected that dysregulated pathways will serve as better biomarkers compared with single genes. |
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 % |
|---|---|---|
| Scientists | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 115 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Brazil | 2 | 2% |
| India | 2 | 2% |
| France | 1 | <1% |
| Korea, Republic of | 1 | <1% |
| Germany | 1 | <1% |
| United Kingdom | 1 | <1% |
| Mexico | 1 | <1% |
| Belgium | 1 | <1% |
| Unknown | 105 | 91% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 45 | 39% |
| Researcher | 22 | 19% |
| Student > Master | 17 | 15% |
| Student > Bachelor | 8 | 7% |
| Student > Doctoral Student | 5 | 4% |
| Other | 12 | 10% |
| Unknown | 6 | 5% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 35 | 30% |
| Computer Science | 24 | 21% |
| Biochemistry, Genetics and Molecular Biology | 23 | 20% |
| Medicine and Dentistry | 4 | 3% |
| Pharmacology, Toxicology and Pharmaceutical Science | 3 | 3% |
| Other | 14 | 12% |
| Unknown | 12 | 10% |
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 12 June 2012.
All research outputs
#15,245,883
of 22,668,244 outputs
Outputs from BMC Bioinformatics
#5,361
of 7,247 outputs
Outputs of similar age
#106,333
of 166,843 outputs
Outputs of similar age from BMC Bioinformatics
#74
of 106 outputs
Altmetric has tracked 22,668,244 research outputs across all sources so far. This one is in the 22nd percentile – i.e., 22% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,247 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. This one is in the 18th percentile – i.e., 18% 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 166,843 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 25th percentile – i.e., 25% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 106 others from the same source and published within six weeks on either side of this one. This one is in the 20th percentile – i.e., 20% of its contemporaries scored the same or lower than it.