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Mendeley readers
Attention Score in Context
| Title |
The shaping and functional consequences of the dosage effect landscape in multiple myeloma
|
|---|---|
| Published in |
BMC Genomics, October 2013
|
| DOI | 10.1186/1471-2164-14-672 |
| Pubmed ID | |
| Authors |
Mehmet K Samur, Parantu K Shah, Xujun Wang, Stéphane Minvielle, Florence Magrangeas, Hervé Avet-Loiseau, Nikhil C Munshi, Cheng Li |
| Abstract |
Multiple myeloma (MM) is a malignant proliferation of plasma B cells. Based on recurrent aneuploidy such as copy number alterations (CNAs), myeloma is divided into two subtypes with different CNA patterns and patient survival outcomes. How aneuploidy events arise, and whether they contribute to cancer cell evolution are actively studied. The large amount of transcriptomic changes resultant of CNAs (dosage effect) pose big challenges for identifying functional consequences of CNAs in myeloma in terms of specific driver genes and pathways. In this study, we hypothesize that gene-wise dosage effect varies as a result from complex regulatory networks that translate the impact of CNAs to gene expression, and studying this variation can provide insights into functional effects of CNAs. |
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 34 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Canada | 1 | 3% |
| Unknown | 33 | 97% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Master | 6 | 18% |
| Researcher | 5 | 15% |
| Other | 3 | 9% |
| Student > Ph. D. Student | 3 | 9% |
| Lecturer | 1 | 3% |
| Other | 4 | 12% |
| Unknown | 12 | 35% |
| Readers by discipline | Count | As % |
|---|---|---|
| Biochemistry, Genetics and Molecular Biology | 5 | 15% |
| Medicine and Dentistry | 5 | 15% |
| Computer Science | 4 | 12% |
| Agricultural and Biological Sciences | 3 | 9% |
| Psychology | 2 | 6% |
| Other | 1 | 3% |
| Unknown | 14 | 41% |
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 04 October 2013.
All research outputs
#18,349,805
of 22,725,280 outputs
Outputs from BMC Genomics
#8,158
of 10,628 outputs
Outputs of similar age
#154,473
of 207,309 outputs
Outputs of similar age from BMC Genomics
#95
of 149 outputs
Altmetric has tracked 22,725,280 research outputs across all sources so far. This one is in the 11th percentile – i.e., 11% of other outputs scored the same or lower than it.
So far Altmetric has tracked 10,628 research outputs from this source. They receive a mean Attention Score of 4.7. This one is in the 12th percentile – i.e., 12% 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 207,309 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 12th percentile – i.e., 12% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 149 others from the same source and published within six weeks on either side of this one. This one is in the 23rd percentile – i.e., 23% of its contemporaries scored the same or lower than it.