| Title |
Machine learning and genomics: precision medicine versus patient privacy
|
|---|---|
| Published in |
Philosophical Transactions of the Royal Society A: Mathematical, Physical & Engineering Sciences, August 2018
|
| DOI | 10.1098/rsta.2017.0350 |
| Pubmed ID | |
| Authors |
C-A Azencott |
| Abstract |
Machine learning can have a major societal impact in computational biology applications. In particular, it plays a central role in the development of precision medicine, whereby treatment is tailored to the clinical or genetic features of the patient. However, these advances require collecting and sharing among researchers large amounts of genomic data, which generates much concern about privacy. Researchers, study participants and governing bodies should be aware of the ways in which the privacy of participants might be compromised, as well as of the large body of research on technical solutions to these issues. We review how breaches in patient privacy can occur, present recent developments in computational data protection and discuss how they can be combined with legal and ethical perspectives to provide secure frameworks for genomic data sharing.This article is part of a discussion meeting issue 'The growing ubiquity of algorithms in society: implications, impacts and innovations'. |
X Demographics
The data shown below were collected from the profiles of 15 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Canada | 2 | 13% |
| United Kingdom | 1 | 7% |
| Switzerland | 1 | 7% |
| Netherlands | 1 | 7% |
| France | 1 | 7% |
| Italy | 1 | 7% |
| United States | 1 | 7% |
| Germany | 1 | 7% |
| Australia | 1 | 7% |
| Other | 0 | 0% |
| Unknown | 5 | 33% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 8 | 53% |
| Scientists | 6 | 40% |
| Practitioners (doctors, other healthcare professionals) | 1 | 7% |
Mendeley readers
The data shown below were compiled from readership statistics for 147 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 147 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Bachelor | 21 | 14% |
| Student > Master | 20 | 14% |
| Student > Ph. D. Student | 16 | 11% |
| Other | 12 | 8% |
| Researcher | 11 | 7% |
| Other | 20 | 14% |
| Unknown | 47 | 32% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 21 | 14% |
| Biochemistry, Genetics and Molecular Biology | 17 | 12% |
| Medicine and Dentistry | 11 | 7% |
| Social Sciences | 9 | 6% |
| Engineering | 7 | 5% |
| Other | 29 | 20% |
| Unknown | 53 | 36% |
Attention Score in Context
This research output has an Altmetric Attention Score of 20. 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 16 June 2022.
All research outputs
#1,903,359
of 25,492,047 outputs
Outputs from Philosophical Transactions of the Royal Society A: Mathematical, Physical & Engineering Sciences
#443
of 3,645 outputs
Outputs of similar age
#38,229
of 340,961 outputs
Outputs of similar age from Philosophical Transactions of the Royal Society A: Mathematical, Physical & Engineering Sciences
#13
of 48 outputs
Altmetric has tracked 25,492,047 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 92nd percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 3,645 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 13.0. This one has done well, scoring higher than 87% 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 340,961 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 88% of its contemporaries.
We're also able to compare this research output to 48 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 75% of its contemporaries.