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X Demographics
Mendeley readers
Attention Score in Context
| Title |
From big data analysis to personalized medicine for all: challenges and opportunities
|
|---|---|
| Published in |
BMC Medical Genomics, June 2015
|
| DOI | 10.1186/s12920-015-0108-y |
| Pubmed ID | |
| Authors |
Akram Alyass, Michelle Turcotte, David Meyre |
| Abstract |
Recent advances in high-throughput technologies have led to the emergence of systems biology as a holistic science to achieve more precise modeling of complex diseases. Many predict the emergence of personalized medicine in the near future. We are, however, moving from two-tiered health systems to a two-tiered personalized medicine. Omics facilities are restricted to affluent regions, and personalized medicine is likely to widen the growing gap in health systems between high and low-income countries. This is mirrored by an increasing lag between our ability to generate and analyze big data. Several bottlenecks slow-down the transition from conventional to personalized medicine: generation of cost-effective high-throughput data; hybrid education and multidisciplinary teams; data storage and processing; data integration and interpretation; and individual and global economic relevance. This review provides an update of important developments in the analysis of big data and forward strategies to accelerate the global transition to personalized medicine. |
X Demographics
The data shown below were collected from the profiles of 31 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United Kingdom | 5 | 16% |
| United States | 3 | 10% |
| France | 3 | 10% |
| Spain | 2 | 6% |
| Germany | 1 | 3% |
| Netherlands | 1 | 3% |
| Canada | 1 | 3% |
| Indonesia | 1 | 3% |
| Unknown | 14 | 45% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 20 | 65% |
| Scientists | 7 | 23% |
| Practitioners (doctors, other healthcare professionals) | 3 | 10% |
| Science communicators (journalists, bloggers, editors) | 1 | 3% |
Mendeley readers
The data shown below were compiled from readership statistics for 860 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 3 | <1% |
| Brazil | 3 | <1% |
| Luxembourg | 2 | <1% |
| Spain | 2 | <1% |
| Canada | 2 | <1% |
| Italy | 1 | <1% |
| Sweden | 1 | <1% |
| France | 1 | <1% |
| Indonesia | 1 | <1% |
| Other | 2 | <1% |
| Unknown | 842 | 98% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 154 | 18% |
| Researcher | 130 | 15% |
| Student > Master | 121 | 14% |
| Student > Bachelor | 89 | 10% |
| Student > Doctoral Student | 44 | 5% |
| Other | 161 | 19% |
| Unknown | 161 | 19% |
| Readers by discipline | Count | As % |
|---|---|---|
| Biochemistry, Genetics and Molecular Biology | 113 | 13% |
| Medicine and Dentistry | 113 | 13% |
| Computer Science | 107 | 12% |
| Agricultural and Biological Sciences | 93 | 11% |
| Engineering | 47 | 5% |
| Other | 192 | 22% |
| Unknown | 195 | 23% |
Attention Score in Context
This research output has an Altmetric Attention Score of 22. 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 30 March 2018.
All research outputs
#1,640,960
of 24,821,035 outputs
Outputs from BMC Medical Genomics
#42
of 1,354 outputs
Outputs of similar age
#20,249
of 268,615 outputs
Outputs of similar age from BMC Medical Genomics
#3
of 30 outputs
Altmetric has tracked 24,821,035 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 93rd percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,354 research outputs from this source. They receive a mean Attention Score of 4.6. This one has done particularly well, scoring higher than 96% 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 268,615 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 92% of its contemporaries.
We're also able to compare this research output to 30 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 93% of its contemporaries.