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X Demographics
Mendeley readers
Attention Score in Context
| Title |
A method for exploring implicit concept relatedness in biomedical knowledge network
|
|---|---|
| Published in |
BMC Bioinformatics, July 2016
|
| DOI | 10.1186/s12859-016-1131-5 |
| Pubmed ID | |
| Authors |
Tian Bai, Leiguang Gong, Ye Wang, Yan Wang, Casimir A. Kulikowski, Lan Huang |
| Abstract |
Biomedical information and knowledge, structural and non-structural, stored in different repositories can be semantically connected to form a hybrid knowledge network. How to compute relatedness between concepts and discover valuable but implicit information or knowledge from it effectively and efficiently is of paramount importance for precision medicine, and a major challenge facing the biomedical research community. In this study, a hybrid biomedical knowledge network is constructed by linking concepts across multiple biomedical ontologies as well as non-structural biomedical knowledge sources. To discover implicit relatedness between concepts in ontologies for which potentially valuable relationships (implicit knowledge) may exist, we developed a Multi-Ontology Relatedness Model (MORM) within the knowledge network, for which a relatedness network (RN) is defined and computed across multiple ontologies using a formal inference mechanism of set-theoretic operations. Semantic constraints are designed and implemented to prune the search space of the relatedness network. Experiments to test examples of several biomedical applications have been carried out, and the evaluation of the results showed an encouraging potential of the proposed approach to biomedical knowledge discovery. |
X Demographics
The data shown below were collected from the profiles of 19 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 | 7 | 37% |
| Argentina | 1 | 5% |
| New Zealand | 1 | 5% |
| Italy | 1 | 5% |
| Chile | 1 | 5% |
| Lithuania | 1 | 5% |
| South Africa | 1 | 5% |
| Unknown | 6 | 32% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 14 | 74% |
| Scientists | 5 | 26% |
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 % |
|---|---|---|
| United States | 1 | 3% |
| Unknown | 33 | 97% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 8 | 24% |
| Researcher | 7 | 21% |
| Student > Master | 5 | 15% |
| Other | 2 | 6% |
| Professor | 1 | 3% |
| Other | 1 | 3% |
| Unknown | 10 | 29% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 12 | 35% |
| Biochemistry, Genetics and Molecular Biology | 4 | 12% |
| Nursing and Health Professions | 2 | 6% |
| Agricultural and Biological Sciences | 2 | 6% |
| Neuroscience | 2 | 6% |
| Other | 2 | 6% |
| Unknown | 10 | 29% |
Attention Score in Context
This research output has an Altmetric Attention Score of 9. 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 01 January 2017.
All research outputs
#4,098,790
of 25,452,734 outputs
Outputs from BMC Bioinformatics
#1,348
of 7,705 outputs
Outputs of similar age
#69,517
of 377,385 outputs
Outputs of similar age from BMC Bioinformatics
#19
of 108 outputs
Altmetric has tracked 25,452,734 research outputs across all sources so far. Compared to these this one has done well and is in the 83rd percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 7,705 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.5. This one has done well, scoring higher than 82% 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 377,385 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 81% of its contemporaries.
We're also able to compare this research output to 108 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 83% of its contemporaries.