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Mendeley readers
Attention Score in Context
| Title |
A Differentiation-Based Phylogeny of Cancer Subtypes
|
|---|---|
| Published in |
PLoS Computational Biology, May 2010
|
| DOI | 10.1371/journal.pcbi.1000777 |
| Pubmed ID | |
| Authors |
Markus Riester, Camille Stephan-Otto Attolini, Robert J. Downey, Samuel Singer, Franziska Michor |
| Abstract |
Histopathological classification of human tumors relies in part on the degree of differentiation of the tumor sample. To date, there is no objective systematic method to categorize tumor subtypes by maturation. In this paper, we introduce a novel computational algorithm to rank tumor subtypes according to the dissimilarity of their gene expression from that of stem cells and fully differentiated tissue, and thereby construct a phylogenetic tree of cancer. We validate our methodology with expression data of leukemia, breast cancer and liposarcoma subtypes and then apply it to a broader group of sarcomas. This ranking of tumor subtypes resulting from the application of our methodology allows the identification of genes correlated with differentiation and may help to identify novel therapeutic targets. Our algorithm represents the first phylogeny-based tool to analyze the differentiation status of human tumors. |
Mendeley readers
The data shown below were compiled from readership statistics for 122 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United Kingdom | 3 | 2% |
| United States | 3 | 2% |
| France | 2 | 2% |
| Spain | 2 | 2% |
| Sweden | 1 | <1% |
| Finland | 1 | <1% |
| Canada | 1 | <1% |
| Italy | 1 | <1% |
| Switzerland | 1 | <1% |
| Other | 1 | <1% |
| Unknown | 106 | 87% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 34 | 28% |
| Researcher | 30 | 25% |
| Student > Master | 13 | 11% |
| Professor > Associate Professor | 10 | 8% |
| Student > Bachelor | 9 | 7% |
| Other | 19 | 16% |
| Unknown | 7 | 6% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 60 | 49% |
| Biochemistry, Genetics and Molecular Biology | 16 | 13% |
| Computer Science | 15 | 12% |
| Medicine and Dentistry | 7 | 6% |
| Immunology and Microbiology | 3 | 2% |
| Other | 11 | 9% |
| Unknown | 10 | 8% |
Attention Score in Context
This research output has an Altmetric Attention Score of 13. 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 22 August 2013.
All research outputs
#2,656,029
of 25,374,917 outputs
Outputs from PLoS Computational Biology
#2,400
of 8,960 outputs
Outputs of similar age
#9,708
of 104,210 outputs
Outputs of similar age from PLoS Computational Biology
#16
of 52 outputs
Altmetric has tracked 25,374,917 research outputs across all sources so far. Compared to these this one has done well and is in the 89th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 8,960 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 20.4. This one has gotten more attention than average, scoring higher than 73% 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 104,210 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 90% of its contemporaries.
We're also able to compare this research output to 52 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 69% of its contemporaries.