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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Differentially private genome data dissemination through top-down specialization
|
|---|---|
| Published in |
BMC Medical Informatics and Decision Making, December 2014
|
| DOI | 10.1186/1472-6947-14-s1-s2 |
| Pubmed ID | |
| Authors |
Shuang Wang, Noman Mohammed, Rui Chen |
| Abstract |
Advanced sequencing techniques make large genome data available at an unprecedented speed and reduced cost. Genome data sharing has the potential to facilitate significant medical breakthroughs. However, privacy concerns have impeded efficient genome data sharing. In this paper, we present a novel approach for disseminating genomic data while satisfying differential privacy. The proposed algorithm splits raw genome sequences into blocks, subdivides the blocks in a top-down fashion, and finally adds noise to counts to preserve privacy. The experimental results suggest that the proposed algorithm can retain certain data utility in terms of a high sensitivity. |
X Demographics
The data shown below were collected from the profiles of 2 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| India | 1 | 50% |
| Unknown | 1 | 50% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 1 | 50% |
| Practitioners (doctors, other healthcare professionals) | 1 | 50% |
Mendeley readers
The data shown below were compiled from readership statistics for 28 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Germany | 1 | 4% |
| Unknown | 27 | 96% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 12 | 43% |
| Professor > Associate Professor | 3 | 11% |
| Student > Master | 3 | 11% |
| Researcher | 2 | 7% |
| Other | 2 | 7% |
| Other | 4 | 14% |
| Unknown | 2 | 7% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 14 | 50% |
| Agricultural and Biological Sciences | 3 | 11% |
| Engineering | 2 | 7% |
| Arts and Humanities | 1 | 4% |
| Medicine and Dentistry | 1 | 4% |
| Other | 1 | 4% |
| Unknown | 6 | 21% |
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 02 January 2015.
All research outputs
#17,734,890
of 22,774,233 outputs
Outputs from BMC Medical Informatics and Decision Making
#1,497
of 1,984 outputs
Outputs of similar age
#247,343
of 360,807 outputs
Outputs of similar age from BMC Medical Informatics and Decision Making
#29
of 34 outputs
Altmetric has tracked 22,774,233 research outputs across all sources so far. This one is in the 19th percentile – i.e., 19% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,984 research outputs from this source. They receive a mean Attention Score of 4.9. This one is in the 21st percentile – i.e., 21% 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 360,807 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 27th percentile – i.e., 27% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 34 others from the same source and published within six weeks on either side of this one. This one is in the 14th percentile – i.e., 14% of its contemporaries scored the same or lower than it.