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Mendeley readers
Attention Score in Context
| Title |
Decision tree-based method for integrating gene expression, demographic, and clinical data to determine disease endotypes
|
|---|---|
| Published in |
BMC Systems Biology, November 2013
|
| DOI | 10.1186/1752-0509-7-119 |
| Pubmed ID | |
| Authors |
ClarLynda R Williams-DeVane, David M Reif, Elaine Cohen Hubal, Pierre R Bushel, Edward E Hudgens, Jane E Gallagher, Stephen W Edwards |
| Abstract |
Complex diseases are often difficult to diagnose, treat and study due to the multi-factorial nature of the underlying etiology. Large data sets are now widely available that can be used to define novel, mechanistically distinct disease subtypes (endotypes) in a completely data-driven manner. However, significant challenges exist with regard to how to segregate individuals into suitable subtypes of the disease and understand the distinct biological mechanisms of each when the goal is to maximize the discovery potential of these data sets. |
X Demographics
The data shown below were collected from the profiles of 5 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 | 2 | 40% |
| Unknown | 3 | 60% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 3 | 60% |
| Scientists | 2 | 40% |
Mendeley readers
The data shown below were compiled from readership statistics for 84 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 3 | 4% |
| Netherlands | 2 | 2% |
| Iran, Islamic Republic of | 1 | 1% |
| Portugal | 1 | 1% |
| Unknown | 77 | 92% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 21 | 25% |
| Student > Ph. D. Student | 15 | 18% |
| Student > Master | 14 | 17% |
| Student > Bachelor | 5 | 6% |
| Other | 5 | 6% |
| Other | 8 | 10% |
| Unknown | 16 | 19% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 12 | 14% |
| Agricultural and Biological Sciences | 12 | 14% |
| Medicine and Dentistry | 9 | 11% |
| Biochemistry, Genetics and Molecular Biology | 8 | 10% |
| Engineering | 8 | 10% |
| Other | 17 | 20% |
| Unknown | 18 | 21% |
Attention Score in Context
This research output has an Altmetric Attention Score of 4. 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 January 2017.
All research outputs
#8,474,955
of 25,374,647 outputs
Outputs from BMC Systems Biology
#315
of 1,132 outputs
Outputs of similar age
#76,804
of 227,931 outputs
Outputs of similar age from BMC Systems Biology
#16
of 57 outputs
Altmetric has tracked 25,374,647 research outputs across all sources so far. This one has received more attention than most of these and is in the 66th percentile.
So far Altmetric has tracked 1,132 research outputs from this source. They receive a mean Attention Score of 3.7. This one has gotten more attention than average, scoring higher than 71% 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 227,931 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 66% of its contemporaries.
We're also able to compare this research output to 57 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 71% of its contemporaries.