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X Demographics
Mendeley readers
Attention Score in Context
| Title |
A network-assisted co-clustering algorithm to discover cancer subtypes based on gene expression
|
|---|---|
| Published in |
BMC Bioinformatics, February 2014
|
| DOI | 10.1186/1471-2105-15-37 |
| Pubmed ID | |
| Authors |
Yiyi Liu, Quanquan Gu, Jack P Hou, Jiawei Han, Jian Ma |
| Abstract |
Cancer subtype information is critically important for understanding tumor heterogeneity. Existing methods to identify cancer subtypes have primarily focused on utilizing generic clustering algorithms (such as hierarchical clustering) to identify subtypes based on gene expression data. The network-level interaction among genes, which is key to understanding the molecular perturbations in cancer, has been rarely considered during the clustering process. The motivation of our work is to develop a method that effectively incorporates molecular interaction networks into the clustering process to improve cancer subtype identification. |
X Demographics
The data shown below were collected from the profiles of 8 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 2 | 25% |
| Sweden | 1 | 13% |
| Canada | 1 | 13% |
| Australia | 1 | 13% |
| Norway | 1 | 13% |
| Unknown | 2 | 25% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 5 | 63% |
| Scientists | 3 | 38% |
Mendeley readers
The data shown below were compiled from readership statistics for 139 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 3 | 2% |
| Netherlands | 1 | <1% |
| Germany | 1 | <1% |
| Slovenia | 1 | <1% |
| United Kingdom | 1 | <1% |
| Unknown | 132 | 95% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 49 | 35% |
| Researcher | 26 | 19% |
| Student > Master | 18 | 13% |
| Student > Postgraduate | 8 | 6% |
| Student > Bachelor | 6 | 4% |
| Other | 15 | 11% |
| Unknown | 17 | 12% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 37 | 27% |
| Agricultural and Biological Sciences | 30 | 22% |
| Biochemistry, Genetics and Molecular Biology | 28 | 20% |
| Medicine and Dentistry | 7 | 5% |
| Mathematics | 4 | 3% |
| Other | 11 | 8% |
| Unknown | 22 | 16% |
Attention Score in Context
This research output has an Altmetric Attention Score of 5. 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 16 June 2014.
All research outputs
#6,477,616
of 23,812,962 outputs
Outputs from BMC Bioinformatics
#2,387
of 7,450 outputs
Outputs of similar age
#74,673
of 311,529 outputs
Outputs of similar age from BMC Bioinformatics
#27
of 100 outputs
Altmetric has tracked 23,812,962 research outputs across all sources so far. This one has received more attention than most of these and is in the 72nd percentile.
So far Altmetric has tracked 7,450 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.5. This one has gotten more attention than average, scoring higher than 67% of its peers.
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 311,529 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 75% of its contemporaries.
We're also able to compare this research output to 100 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 73% of its contemporaries.