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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Semi-automated literature mining to identify putative biomarkers of disease from multiple biofluids
|
|---|---|
| Published in |
Journal of Clinical Bioinformatics, October 2014
|
| DOI | 10.1186/2043-9113-4-13 |
| Pubmed ID | |
| Authors |
Rick Jordan, Shyam Visweswaran, Vanathi Gopalakrishnan |
| Abstract |
Computational methods for mining of biomedical literature can be useful in augmenting manual searches of the literature using keywords for disease-specific biomarker discovery from biofluids. In this work, we develop and apply a semi-automated literature mining method to mine abstracts obtained from PubMed to discover putative biomarkers of breast and lung cancers in specific biofluids. |
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 % |
|---|---|---|
| Unknown | 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 34 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Hungary | 1 | 3% |
| Switzerland | 1 | 3% |
| Unknown | 32 | 94% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Bachelor | 6 | 18% |
| Researcher | 6 | 18% |
| Student > Ph. D. Student | 6 | 18% |
| Other | 3 | 9% |
| Student > Master | 3 | 9% |
| Other | 3 | 9% |
| Unknown | 7 | 21% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 8 | 24% |
| Medicine and Dentistry | 5 | 15% |
| Biochemistry, Genetics and Molecular Biology | 3 | 9% |
| Computer Science | 2 | 6% |
| Immunology and Microbiology | 2 | 6% |
| Other | 5 | 15% |
| Unknown | 9 | 26% |
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 21 November 2014.
All research outputs
#20,656,161
of 25,373,627 outputs
Outputs from Journal of Clinical Bioinformatics
#44
of 61 outputs
Outputs of similar age
#200,180
of 273,327 outputs
Outputs of similar age from Journal of Clinical Bioinformatics
#3
of 3 outputs
Altmetric has tracked 25,373,627 research outputs across all sources so far. This one is in the 10th percentile – i.e., 10% of other outputs scored the same or lower than it.
So far Altmetric has tracked 61 research outputs from this source. They receive a mean Attention Score of 3.1. This one is in the 3rd percentile – i.e., 3% 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 273,327 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 14th percentile – i.e., 14% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 3 others from the same source and published within six weeks on either side of this one.