You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output.
Click here to find out more.
X Demographics
Mendeley readers
Attention Score in Context
| Title |
Mining hidden knowledge for drug safety assessment: topic modeling of LiverTox as a case study
|
|---|---|
| Published in |
BMC Bioinformatics, December 2014
|
| DOI | 10.1186/1471-2105-15-s17-s6 |
| Pubmed ID | |
| Authors |
Ke Yu, Jie Zhang, Minjun Chen, Xiaowei Xu, Ayako Suzuki, Katarina Ilic, Weida Tong |
| Abstract |
Given the significant impact on public health and drug development, drug safety has been a focal point and research emphasis across multiple disciplines in addition to scientific investigation, including consumer advocates, drug developers and regulators. Such a concern and effort has led numerous databases with drug safety information available in the public domain and the majority of them contain substantial textual data. Text mining offers an opportunity to leverage the hidden knowledge within these textual data for the enhanced understanding of drug safety and thus improving public health. |
X Demographics
The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Switzerland | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 55 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Canada | 1 | 2% |
| Unknown | 54 | 98% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 10 | 18% |
| Researcher | 9 | 16% |
| Student > Master | 8 | 15% |
| Student > Bachelor | 8 | 15% |
| Student > Doctoral Student | 2 | 4% |
| Other | 9 | 16% |
| Unknown | 9 | 16% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 13 | 24% |
| Medicine and Dentistry | 11 | 20% |
| Engineering | 5 | 9% |
| Agricultural and Biological Sciences | 4 | 7% |
| Biochemistry, Genetics and Molecular Biology | 2 | 4% |
| Other | 8 | 15% |
| Unknown | 12 | 22% |
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 12 January 2015.
All research outputs
#18,388,295
of 22,776,824 outputs
Outputs from BMC Bioinformatics
#6,308
of 7,276 outputs
Outputs of similar age
#256,502
of 354,395 outputs
Outputs of similar age from BMC Bioinformatics
#138
of 150 outputs
Altmetric has tracked 22,776,824 research outputs across all sources so far. This one is in the 11th percentile – i.e., 11% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,276 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. This one is in the 5th percentile – i.e., 5% 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 354,395 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 16th percentile – i.e., 16% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 150 others from the same source and published within six weeks on either side of this one. This one is in the 3rd percentile – i.e., 3% of its contemporaries scored the same or lower than it.