| Title |
An unsupervised learning approach to identify novel signatures of health and disease from multimodal data
|
|---|---|
| Published in |
Genome Medicine, January 2020
|
| DOI | 10.1186/s13073-019-0705-z |
| Pubmed ID | |
| Authors |
Ilan Shomorony, Elizabeth T. Cirulli, Lei Huang, Lori A. Napier, Robyn R. Heister, Michael Hicks, Isaac V. Cohen, Hung-Chun Yu, Christine Leon Swisher, Natalie M. Schenker-Ahmed, Weizhong Li, Karen E. Nelson, Pamila Brar, Andrew M. Kahn, Timothy D. Spector, C. Thomas Caskey, J. Craig Venter, David S. Karow, Ewen F. Kirkness, Naisha Shah |
X Demographics
The data shown below were collected from the profiles of 24 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 States | 5 | 21% |
| Germany | 2 | 8% |
| Italy | 1 | 4% |
| Belarus | 1 | 4% |
| Singapore | 1 | 4% |
| United Kingdom | 1 | 4% |
| Georgia | 1 | 4% |
| Israel | 1 | 4% |
| Canada | 1 | 4% |
| Other | 0 | 0% |
| Unknown | 10 | 42% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 19 | 79% |
| Scientists | 3 | 13% |
| Practitioners (doctors, other healthcare professionals) | 2 | 8% |
Mendeley readers
The data shown below were compiled from readership statistics for 129 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 129 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 24 | 19% |
| Student > Ph. D. Student | 17 | 13% |
| Student > Master | 16 | 12% |
| Student > Bachelor | 12 | 9% |
| Student > Doctoral Student | 6 | 5% |
| Other | 14 | 11% |
| Unknown | 40 | 31% |
| Readers by discipline | Count | As % |
|---|---|---|
| Medicine and Dentistry | 14 | 11% |
| Biochemistry, Genetics and Molecular Biology | 13 | 10% |
| Agricultural and Biological Sciences | 11 | 9% |
| Computer Science | 7 | 5% |
| Pharmacology, Toxicology and Pharmaceutical Science | 6 | 5% |
| Other | 30 | 23% |
| Unknown | 48 | 37% |
Attention Score in Context
This research output has an Altmetric Attention Score of 14. 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 05 June 2020.
All research outputs
#2,548,639
of 25,515,042 outputs
Outputs from Genome Medicine
#572
of 1,592 outputs
Outputs of similar age
#60,453
of 477,098 outputs
Outputs of similar age from Genome Medicine
#15
of 35 outputs
Altmetric has tracked 25,515,042 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 1,592 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 26.7. This one has gotten more attention than average, scoring higher than 64% 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 477,098 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 87% of its contemporaries.
We're also able to compare this research output to 35 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 60% of its contemporaries.