| Title |
ARN: analysis and prediction by adipogenic professional database
|
|---|---|
| Published in |
BMC Systems Biology, August 2016
|
| DOI | 10.1186/s12918-016-0321-0 |
| Pubmed ID | |
| Authors |
Yan Huang, Li Wang, and Lin-sen Zan |
| Abstract |
Adipogenesis is the process of cell differentiation by which mesenchymal stem cells become adipocytes. Extensive research is ongoing to identify genes, their protein products, and microRNAs that correlate with fat cell development. The existing databases have focused on certain types of regulatory factors and interactions. However, there is no relationship between the results of the experimental studies on adipogenesis and these databases because of the lack of an information center. This information fragmentation hampers the identification of key regulatory genes and pathways. Thus, it is necessary to provide an information center that is quickly and easily accessible to researchers in this field. We selected and integrated data from eight external databases based on the results of text-mining, and constructed a publicly available database and web interface (URL: http://210.27.80.93/arn/ ), which contained 30873 records related to adipogenic differentiation. Then, we designed an online analysis tool to analyze the experimental data or form a scientific hypothesis about adipogenesis through Swanson's literature-based discovery process. Furthermore, we calculated the "Impact Factor" ("IF") value that reflects the importance of each node by counting the numbers of relation records, expression records, and prediction records for each node. This platform can support ongoing adipogenesis research and contribute to the discovery of key regulatory genes and pathways. |
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 21 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 21 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Bachelor | 5 | 24% |
| Student > Doctoral Student | 4 | 19% |
| Student > Ph. D. Student | 3 | 14% |
| Other | 2 | 10% |
| Researcher | 2 | 10% |
| Other | 2 | 10% |
| Unknown | 3 | 14% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 6 | 29% |
| Biochemistry, Genetics and Molecular Biology | 5 | 24% |
| Agricultural and Biological Sciences | 4 | 19% |
| Engineering | 3 | 14% |
| Medicine and Dentistry | 1 | 5% |
| Other | 0 | 0% |
| Unknown | 2 | 10% |
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 10 August 2016.
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#20,337,210
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Outputs from BMC Systems Biology
#1,009
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Outputs of similar age
#319,401
of 364,241 outputs
Outputs of similar age from BMC Systems Biology
#26
of 31 outputs
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