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Molecular classification of cutaneous malignant melanoma by gene expression profiling

Overview of attention for article published in Nature, August 2000
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (94th percentile)
  • Good Attention Score compared to outputs of the same age and source (78th percentile)

Mentioned by

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1 policy source
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1 X user
patent
97 patents

Citations

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1648 Dimensions

Readers on

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490 Mendeley
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3 CiteULike
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1 Connotea
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Title
Molecular classification of cutaneous malignant melanoma by gene expression profiling
Published in
Nature, August 2000
DOI 10.1038/35020115
Pubmed ID
Authors

M. Bittner, P. Meltzer, Y. Chen, Y. Jiang, E. Seftor, M. Hendrix, M. Radmacher, R. Simon, Z. Yakhini, A. Ben-Dor, N. Sampas, E. Dougherty, E. Wang, F. Marincola, C. Gooden, J. Lueders, A. Glatfelter, P. Pollock, J. Carpten, E. Gillanders, D. Leja, K. Dietrich, C. Beaudry, M. Berens, D. Alberts, V. Sondak, N. Hayward, J. Trent

Abstract

The most common human cancers are malignant neoplasms of the skin. Incidence of cutaneous melanoma is rising especially steeply, with minimal progress in non-surgical treatment of advanced disease. Despite significant effort to identify independent predictors of melanoma outcome, no accepted histopathological, molecular or immunohistochemical marker defines subsets of this neoplasm. Accordingly, though melanoma is thought to present with different 'taxonomic' forms, these are considered part of a continuous spectrum rather than discrete entities. Here we report the discovery of a subset of melanomas identified by mathematical analysis of gene expression in a series of samples. Remarkably, many genes underlying the classification of this subset are differentially regulated in invasive melanomas that form primitive tubular networks in vitro, a feature of some highly aggressive metastatic melanomas. Global transcript analysis can identify unrecognized subtypes of cutaneous melanoma and predict experimentally verifiable phenotypic characteristics that may be of importance to disease progression.

X Demographics

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.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 490 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 9 2%
United Kingdom 4 <1%
Switzerland 2 <1%
Brazil 2 <1%
Poland 2 <1%
Japan 2 <1%
Denmark 2 <1%
New Zealand 1 <1%
Canada 1 <1%
Other 5 1%
Unknown 460 94%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 110 22%
Researcher 104 21%
Student > Master 60 12%
Professor > Associate Professor 32 7%
Student > Bachelor 30 6%
Other 99 20%
Unknown 55 11%
Readers by discipline Count As %
Agricultural and Biological Sciences 169 34%
Biochemistry, Genetics and Molecular Biology 86 18%
Medicine and Dentistry 60 12%
Computer Science 33 7%
Engineering 14 3%
Other 58 12%
Unknown 70 14%
Attention Score in Context

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 23 January 2024.
All research outputs
#2,352,931
of 23,342,092 outputs
Outputs from Nature
#43,760
of 92,113 outputs
Outputs of similar age
#1,926
of 38,114 outputs
Outputs of similar age from Nature
#64
of 312 outputs
Altmetric has tracked 23,342,092 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 92,113 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 100.1. This one has gotten more attention than average, scoring higher than 52% 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 38,114 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 94% of its contemporaries.
We're also able to compare this research output to 312 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 78% of its contemporaries.