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Mendeley readers
Attention Score in Context
| Title |
Morphometic analysis of TCGA glioblastoma multiforme
|
|---|---|
| Published in |
BMC Bioinformatics, December 2011
|
| DOI | 10.1186/1471-2105-12-484 |
| Pubmed ID | |
| Authors |
Hang Chang, Gerald V Fontenay, Ju Han, Ge Cong, Frederick L Baehner, Joe W Gray, Paul T Spellman, Bahram Parvin |
| Abstract |
Our goals are to develop a computational histopathology pipeline for characterizing tumor types that are being generated by The Cancer Genome Atlas (TCGA) for genomic association. TCGA is a national collaborative program where different tumor types are being collected, and each tumor is being characterized using a variety of genome-wide platforms. Here, we have developed a tumor-centric analytical pipeline to process tissue sections stained with hematoxylin and eosin (H&E) for visualization and cell-by-cell quantitative analysis. Thus far, analysis is limited to Glioblastoma Multiforme (GBM) and kidney renal clear cell carcinoma tissue sections. The final results are being distributed for subtyping and linking the histology sections to the genomic data. |
Mendeley readers
The data shown below were compiled from readership statistics for 90 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 4 | 4% |
| Germany | 2 | 2% |
| Brazil | 2 | 2% |
| Colombia | 1 | 1% |
| Spain | 1 | 1% |
| United Kingdom | 1 | 1% |
| Unknown | 79 | 88% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 24 | 27% |
| Student > Ph. D. Student | 22 | 24% |
| Student > Master | 9 | 10% |
| Student > Bachelor | 6 | 7% |
| Professor > Associate Professor | 6 | 7% |
| Other | 16 | 18% |
| Unknown | 7 | 8% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 19 | 21% |
| Biochemistry, Genetics and Molecular Biology | 15 | 17% |
| Agricultural and Biological Sciences | 15 | 17% |
| Medicine and Dentistry | 13 | 14% |
| Engineering | 8 | 9% |
| Other | 7 | 8% |
| Unknown | 13 | 14% |
Attention Score in Context
This research output has an Altmetric Attention Score of 3. 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 15 September 2020.
All research outputs
#7,411,203
of 22,659,164 outputs
Outputs from BMC Bioinformatics
#3,023
of 7,240 outputs
Outputs of similar age
#70,489
of 243,104 outputs
Outputs of similar age from BMC Bioinformatics
#50
of 100 outputs
Altmetric has tracked 22,659,164 research outputs across all sources so far. This one is in the 44th percentile – i.e., 44% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,240 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. This one has gotten more attention than average, scoring higher than 50% 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 243,104 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 42nd percentile – i.e., 42% of its contemporaries scored the same or lower than it.
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 is in the 46th percentile – i.e., 46% of its contemporaries scored the same or lower than it.