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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Role of clinical bioinformatics in the development of network-based Biomarkers
|
|---|---|
| Published in |
Journal of Clinical Bioinformatics, October 2011
|
| DOI | 10.1186/2043-9113-1-28 |
| Pubmed ID | |
| Authors |
Xiangdong Wang |
| Abstract |
Network biomarker as a new type of biomarkers with protein-protein interactions was initiated and investigated with the integration of knowledge on protein annotations, interaction, and signaling pathway. A number of methodologies and computational programs have been developed to integrate selected proteins into the knowledge-based networks via the combination of genomics, proteomics and bioinformatics. Alterations of network biomarkers can be monitored and evaluated at different stages and time points during the development of diseases, named dynamic network biomarkers. Dynamic network biomarkers should be furthermore correlated with clinical informatics, including patient complaints, history, therapies, clinical symptoms and signs, physician's examinations, biochemical analyses, imaging profiles, pathologies and other measurements. |
X Demographics
The data shown below were collected from the profiles of 6 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Egypt | 1 | 17% |
| Unknown | 5 | 83% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 6 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 57 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Indonesia | 1 | 2% |
| Spain | 1 | 2% |
| United States | 1 | 2% |
| Italy | 1 | 2% |
| Unknown | 53 | 93% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 14 | 25% |
| Student > Ph. D. Student | 10 | 18% |
| Student > Master | 7 | 12% |
| Student > Bachelor | 5 | 9% |
| Other | 3 | 5% |
| Other | 6 | 11% |
| Unknown | 12 | 21% |
| Readers by discipline | Count | As % |
|---|---|---|
| Biochemistry, Genetics and Molecular Biology | 13 | 23% |
| Agricultural and Biological Sciences | 11 | 19% |
| Computer Science | 9 | 16% |
| Medicine and Dentistry | 3 | 5% |
| Immunology and Microbiology | 2 | 4% |
| Other | 4 | 7% |
| Unknown | 15 | 26% |
Attention Score in Context
This research output has an Altmetric Attention Score of 7. 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 30 November 2011.
All research outputs
#5,404,723
of 25,371,288 outputs
Outputs from Journal of Clinical Bioinformatics
#8
of 61 outputs
Outputs of similar age
#30,489
of 152,378 outputs
Outputs of similar age from Journal of Clinical Bioinformatics
#2
of 7 outputs
Altmetric has tracked 25,371,288 research outputs across all sources so far. Compared to these this one has done well and is in the 78th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 61 research outputs from this source. They receive a mean Attention Score of 3.1. This one has done well, scoring higher than 86% 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 152,378 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 79% of its contemporaries.
We're also able to compare this research output to 7 others from the same source and published within six weeks on either side of this one. This one has scored higher than 5 of them.